How AI augmentation is modernising Smart Follow underwriting in the London Market

Key takeaways on BI in Insurance:

• Business Intelligence enables insurance companies to transform vast amounts of structured and unstructured data into actionable insights, enhancing efficiency in underwriting, claims management, risk assessment, and customer engagement. By identifying trends and forecasting outcomes, BI helps insurers optimise operations and reduce costs.
• Effective BI relies on consolidating diverse data types, including customer demographics and policy history, detailed claims records, external market trends and economic indicators, and internal operational metrics.
• Implementing BI solutions in the insurance industry involves leveraging automation and artificial intelligence to streamline processes such as underwriting and claims assessment.
• While BI offers significant advantages, insurers may face challenges during implementation: issues with data quality and integration, ensuring data security and privacy, overcoming resistance to change within the organisation, and navigating complex regulatory compliance requirements.

What is business intelligence in the context of insurance?

In the insurance industry, business intelligence (BI) encompasses a suite of technologies, processes, tools and methodologies that collect, analyse, and visualise data to support strategic decision-making.

Business intelligence allows insurance companies to transform vast amounts of structured and unstructured data into actionable insights, driving efficiency in underwriting, claims management, risk management, and customer engagement.

Advanced data visualisation and BI dashboards prepared with specific organisational needs in mind make crucial information highly accessible and reliable.

By identifying trends, forecasting outcomes, and optimising operations, business intelligence empowers insurance companies to enhance profitability, reduce operational costs, and comply with evolving regulatory standards. It allows them to make quicker, but simultaneously better and more informative risk assessment decisions.

What types of data are essential for effective BI in insurance business?

Effective business intelligence in the insurance industry relies on the collection, preparation, analysis and reporting of diverse business data types, each playing a critical role in generating insights.

Key data categories include:

• Customer data: demographics, policy history, and interaction records provide insights into customer expectations, behaviour and opportunities for personalised engagement.
• Claims data: detailed records of claims help identify trends, detect insurance fraud, and improve claims management processes to reduce costs and enhance efficiency.
• External data: market trends, economic indicators, and regulatory updates help insurance companies forecast risks, assess competition, and adapt to industry changes.
• Operational data: metrics such as underwriting efficiency and claims processing times offer visibility into internal workflows, enabling process optimisation.

By consolidating and analysing these diverse data sets, insurers can manage risks more effectively, improve decision-making, and create value for customers. BI tools play a pivotal role in facilitating this consolidation, making it possible to derive actionable insights from complex datasets.

MVP for streamlined claims management process

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We align the tool with the claim handlers’ specific needs leveraging continuous UX consultancy. The MVP has received positive feedback from claims handlers.

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Business intelligence in the insurance industry: solutions and technologies

Business intelligence solutions are revolutionising the insurance landscape, delivering value across multiple dimensions:

Automation and Artificial Intelligence (AI)

Automation and AI are cornerstones of BI-driven innovation. By automating repetitive tasks like underwriting and claims assessment, insurers reduce errors and operational costs while accelerating workflows.

AI-powered tools also enhance decision-making by providing predictive insights, optimising pricing models, and personalising offerings.

Risk assessment

Predictive analytics – a key BI component – allows insurers to analyse historical data, recognise patterns, and forecast risks.

This improves policy pricing accuracy, strengthens underwriting, and enhances risk mitigation strategies. Emerging risks like climate change and geopolitical instability can also be addressed using predictive models.

Customer experience

Customer data analysis through BI facilitates personalisation, targeted marketing, and proactive support. Insurers can create tailored offerings, respond in real time, and foster stronger customer loyalty.

Dynamic dashboards provide actionable insights into customer satisfaction metrics, guiding improvements.

Detecting and preventing insurance fraud

BI tools powered by machine learning detect anomalies in claims data, identifying potentially fraudulent activities.

By automating fraud detection, insurers safeguard profitability, accelerate legitimate claims processing, and enhance trust.

Optimising claims management

Real-time analytics streamline claims workflows, reducing bottlenecks and improving efficiency. This not only shortens processing times but also enhances customer satisfaction.

Advanced BI tools highlight inefficiencies and recommend corrective actions, ensuring smoother operations.

Developing new insurance products

BI enables insurance companies to analyse emerging insurance market trends and customer behaviour, identifying opportunities for new, customised insurance products. This helps insurers address coverage gaps and tap into untapped market segments.

By using business intelligence software, insurance companies can simulate different scenarios and model the potential success of new products.

Additional resources on Business Intelligence:

• How Cloud Business Intelligence is revolutionising data analysis?
• Business Intelligence: unlock the potential of your data and put it to use
• Business Intelligence vs. Data Science

What is the impact of BI on underwriting processes?

BI has revolutionised underwriting by replacing manual processes with data-driven insights. Insurers can now leverage real-time data, including historical claims records and external market trends, to make more accurate risk assessments.

Predictive analytics identifies customer behaviour patterns, enabling tailored policy terms and pricing. This approach accelerates the underwriting process, reduces turnaround times, and improves customer satisfaction while ensuring profitability through informed decision-making.

What are the challenges of implementing BI in insurance companies?

Despite its potential, implementing BI in the insurance industry comes with challenges, including:

• Data quality and integration: insurers often struggle to integrate disparate data sources, such as legacy systems and unstructured data. Inconsistent or inaccurate data undermines BI effectiveness.
• Data security and privacy: aggregating sensitive customer data raises concerns about security and compliance with regulations such as GDPR and HIPAA.
• Resistance to change: employees may resist adopting business intelligence tools due to a lack of familiarity or fear of job displacement.
• Analytics complexity: effective use of business intelligence tools requires technical expertise, which may be lacking in some organisations.
• Regulatory compliance: ensuring that BI systems comply with complex insurance business regulations can delay implementation and require additional resources.

How can insurers ensure successful business intelligence implementation?

To ensure successful business intelligence implementation in the insurance sector, insurers must adopt a strategic, well-coordinated approach that aligns with their business objectives and organisational culture.

The first step is to establish a clear vision and roadmap for BI adoption, including defining specific goals, expected outcomes, and key performance indicators (KPIs) to measure success.

Additionally, securing executive support and fostering cross-departmental collaboration is crucial for driving adoption and aligning insurance business intelligence systems with broader business strategies.

Insurers should also invest in the right technology stack, ensuring that BI tools are scalable, flexible, and capable of integrating with existing systems. Training and upskilling employees are essential to ensure that staff can effectively use BI tools and interpret data insights.

Regular monitoring, feedback loops, and continuous improvement processes will also help refine BI systems, ensuring they remain relevant and effective as business needs evolve.

Finally, focusing on data quality, governance, and security will safeguard the integrity of the BI process, enabling insurers to make informed, compliant, and reliable decisions.

With a thoughtful and structured approach, insurers can maximise the benefits of BI and transform their operations for long-term success.

Business intelligence: your cornerstone of success

Business intelligence has become a cornerstone of innovation in the insurance industry. By leveraging advanced analytics, automation, and AI, insurers can enhance efficiency, personalise customer experiences, and mitigate risks.

Despite the challenges, a thoughtful implementation strategy ensures that BI delivers long-term success, transforming insurance operations and positioning companies for future growth.

If your organisation is ready to unlock the full potential of business intelligence and drive innovation in your insurance processes, Future Processing can help.

Contact us today to start building a smarter, data-driven future for your insurance business.

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What is AI in insurance and how does it work?

AI in insurance refers to the use of advanced algorithms and machine learning models to automate processes, analyse vast amounts of data, and provide actionable insights. These technologies enable insurers to improve various aspects of their operations, from underwriting and claims processing to customer service and fraud detection.

AI in insurance works by harnessing data-driven insights and automating processes to enhance efficiency, reduce costs, and improve customer satisfaction. As AI technologies continue to evolve, their impact on the insurance industry is expected to grow, leading to more innovative solutions and a better overall experience for both insurers and policyholders.

Read more about new technologies in insurance:

• 5 key insights from the Digital Transformation in Insurance Press Roundtable
• NLP in the insurance industry: top 5 use cases and benefits
• Data-driven insurance: exploring the latest trends

What are the key benefits of using artificial intelligence in the insurance sector?

Artificial intelligence offers numerous benefits in the insurance sector, enhancing various aspects of operations and delivering significant advantages to both insurers and policyholders.

Key benefits of using AI in the insurance industry include:

1. Improved efficiency and speed:
   • AI automates routine tasks such as data entry, claims processing, and underwriting, which significantly reduces the time required for these processes. This leads to faster response times and improved customer satisfaction.
   • Machine learning models can rapidly analyse large datasets to make quick and accurate decisions, enhancing overall operational efficiency.
2. Enhanced accuracy in underwriting:
   • AI algorithms can process and analyse vast amounts of data, including historical claims and customer information, to assess risk with greater precision. This results in more accurate underwriting and appropriate premium pricing.
   • Predictive analytics enable insurers to foresee potential risks and adjust their strategies accordingly.
3. Fraud detection and prevention:
   • AI systems utilise advanced pattern recognition to detect fraudulent activities by identifying anomalies and unusual patterns in claims data. This proactive approach helps insurers prevent fraud and reduce financial losses.
   • Continuous learning capabilities of AI models improve their effectiveness in identifying new types of fraud over time.
4. Personalised customer experience:
   • AI-driven chatbots and virtual assistants provide personalised and timely customer support, assisting with policy inquiries, claims filing, and other services. This enhances customer engagement and satisfaction.
   • AI can also tailor insurance products to meet individual customer needs, offering personalised recommendations based on data analysis.
5. Cost reduction:
   • By automating manual processes and improving efficiency, AI helps reduce operational costs for insurers. This cost-saving can be passed on to customers in the form of lower premiums.
   • AI’s ability to accurately assess risk and prevent fraud further contributes to cost savings.
6. Data-driven decision making:
   • AI enables insurers to leverage big data and analytics to make informed decisions. This data-driven approach enhances strategic planning, risk management, and marketing efforts.
   • Insights gained from AI analytics help insurers better understand market trends and customer behaviour, allowing for more effective business strategies.

How can AI transform the claims processing experience in insurance?

Artificial intelligence has the potential to profoundly transform the claims processing experience in the insurance industry by enhancing speed, accuracy, and customer satisfaction.

AI-driven systems can automate the initial claims filing process, allowing customers to submit claims through intuitive digital platforms. Once submitted, AI algorithms swiftly analyse the data, cross-referencing with policy details and historical claims to determine the validity and extent of the claim. This automation reduces the manual workload on human adjusters, enabling faster and more consistent decision-making.

Moreover, AI’s machine learning capabilities help in identifying fraudulent claims by detecting patterns and anomalies that may indicate suspicious activity, thus safeguarding insurers against potential losses.

AI-powered chatbots and virtual assistants provide 24/7 support, guiding customers through the claims process, answering queries, and keeping them updated on the status of their claims, which enhances the overall customer experience. By streamlining these processes, AI not only expedites claims resolution, but also ensures a more transparent and efficient claims handling experience for both insurers and policyholders.

What role does AI play in risk assessment and underwriting?

AI plays a pivotal role in risk assessment and underwriting within the insurance industry by leveraging advanced data analytics and machine learning algorithms to enhance precision and efficiency.

By analysing vast amounts of data, including historical claims, demographic information, and external data sources, AI can identify patterns and correlations that human underwriters might miss. This enables insurers to assess risk more accurately and develop more tailored and competitively priced insurance products​​.

Moreover, AI’s predictive analytics capabilities allow for the forecasting of potential future risks based on current trends and behaviours. This forward-looking approach helps insurers to proactively manage risk and adjust their underwriting criteria accordingly.

AI can also streamline the underwriting process by automating routine tasks, such as data entry and initial risk assessments, thereby reducing the time and cost associated with manual underwriting procedures.

Additionally, AI enhances consistency in underwriting decisions, minimising the variability that can occur with human judgment. This ensures a more standardised and fair assessment of risk across all policy applications.

Overall, AI’s integration into risk assessment and underwriting not only improves operational efficiency, but also leads to more accurate pricing and better risk management, ultimately benefiting both insurers and policyholders​.

Can AI help in detecting and preventing insurance fraud?

AI can significantly help in detecting and preventing insurance fraud by utilising advanced machine learning algorithms and data analytics.

AI systems analyse vast amounts of claims data in real-time, identifying patterns and anomalies that may indicate fraudulent activity. For instance, AI can cross-reference new claims with historical data to spot inconsistencies or unusual behaviour, such as repeated claims from the same individual or unusually high claim amounts.

Additionally, AI’s predictive capabilities can forecast potential fraud risks, enabling insurers to take preventive measures before fraud occurs. By automating these processes, AI not only enhances the accuracy of fraud detection but also reduces the time and resources needed for investigations, ultimately saving insurers significant costs and improving the overall integrity of the insurance system.

How does AI contribute to personalising insurance policies for customers?

AI contributes to personalising insurance policies for customers by analysing vast amounts of data to understand individual needs and preferences. Machine learning algorithms evaluate customer behaviour, demographics, and historical data to identify specific risk factors and coverage requirements. This allows insurers to offer tailored policy options that align with each customer’s unique profile, enhancing customer satisfaction and loyalty​​.

Moreover, AI-driven insights enable insurers to anticipate future needs and provide proactive recommendations, ensuring that customers receive the most relevant and beneficial coverage​. By delivering personalised experiences, AI helps insurers build stronger relationships with their clients and improve overall service quality.

What are the cost implications of implementing AI in insurance?

Implementing AI in insurance involves significant initial investments in technology infrastructure, software development, and talent acquisition. Insurers must allocate funds for purchasing or developing AI systems, integrating them with existing processes, and training staff to use these new tools effectively.

Additionally, before introducing AI, an organisation must first prepare the data and processes that will power the AI, ideally by means of data standardisation and harmonisation. Otherwise, there is a risk it will be exposed to erroneous results and so-called AI hallucinations.

However, these upfront costs can be offset by long-term savings and increased efficiency. AI reduces operational costs by automating routine tasks, accelerating claims processing, and enhancing fraud detection, leading to fewer losses and improved risk management.

Additionally, AI-driven personalisation can enhance customer satisfaction and retention, potentially increasing revenue. Overall, while the initial financial outlay is substantial, the return on investment through cost savings and revenue growth can be significant.

What future trends are emerging with AI in the insurance industry?

Future trends in the insurance industry with AI are poised to revolutionise how insurers operate and interact with customers. One key trend is the increasing use of predictive analytics to anticipate customer needs and market changes, allowing for more proactive risk management and personalised offerings.

Additionally, AI-powered chatbots and virtual assistants are becoming more sophisticated, providing real-time support and enhancing customer engagement.

Another emerging trend is the integration of AI with IoT/IoE devices, enabling insurers to gather real-time data on insured assets, which can improve underwriting accuracy and risk assessment.

Furthermore, the adoption of blockchain technology combined with AI is expected to enhance transparency and security in claims processing and fraud prevention, driving greater trust and efficiency in the insurance ecosystem.

AI-driven auto settlement of claims, based on detailed AI analysis, is also becoming increasingly prevalent, reducing processing times and improving customer satisfaction.

How can insurers prepare for AI changes?

Insurers can prepare for AI changes by embracing a dual approach of innovation and regulation.

Firstly, investing in AI technologies such as machine learning algorithms can enhance underwriting accuracy, claims processing efficiency, and customer service personalisation. This involves developing robust data strategies to ensure AI models are trained on quality data and regularly updated to remain relevant.

Secondly, adapting to regulatory frameworks (such as Blueprint 2 in the London Market) is crucial to ensure AI implementation complies with industry standards and data protection laws, thereby fostering trust and transparency with policyholders.

Incorporating a human-in-the-loop element can further ensure that AI decisions are continuously monitored and validated by experienced professionals, adding an extra layer of oversight and accountability.

By fostering a culture of continuous learning and collaboration between AI experts, data scientists, and regulatory professionals, insurers can effectively harness AI’s transformative potential while navigating regulatory challenges.

IT Insights: InsurTalk

Learn more about the latest trends, challenges that leaders face, and the future of insurance.

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Just like in other sectors, for example, finance, NLP entered insurance industry some time ago, and it is gaining more and more attention, revolutionising various aspects of the business.

Natural Language Processing is used in:

• customer service and support,
• in claims processing,
• underwriting and risk assessment,
• fraud detection, policy recommendation,
• compliance and regulatory reporting,
• customer sentiment analysis,
• document summarisation,
• risk communication,
• emergency response.

With so many different use cases, NLP is enabling the insurance industry to streamline their processes and enhance customer experience while reducing costs and allowing for better, data-driven decisions and more efficient work.

Top 5 game-changing use cases of NLP in the insurance industry

To truly understand the extent to which NLP is a game changer in the insurance world, let’s focus on the five important use cases, showing what exactly they mean and how beneficial they are in the industry.

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Streamlining claims processing with NLP-driven automation

Streamlining claims processing with NLP-driven automation leverages NLP to improve the efficiency and accuracy of handling insurance claims.

Instead of dealing with claims manually, NLP algorithms are used to extract relevant information (such as policyholder data, claim descriptions, dates, and other critical details) from unstructured data sources: claim forms, emails, and documents.

Once done, they automatically categorise and prioritise claims based on their severity and complexity, ensuring that urgent or complex claims receive prompt attention while routine claims are processed efficiently.

NLP also helps identify potential instances of fraud by analysing claim descriptions and comparing them with historical data and known fraud patterns. This method prevents fraudulent claims from progressing further in the process.

What’s more, NLP assist claims adjusters by providing them with relevant information from historical claims and policy documents.

Once a claim is approved, NLP can initiate the process of settlement and payment. It can calculate the amount to be paid based on policy terms, coverage, and claim details, ensuring accurate and timely disbursements. All finished, it can generate detailed and structured reports about claims processing.

In summary, NLP-driven automation in claims processing reduces manual work, accelerates processing times and minimises human error, allowing for more efficient and better operations.

NLP for enhanced customer support and virtual assistance

NLP keeps enhancing customer support literally everywhere, so it’s no wonder the insurance world is taking advantage of it as well.

Insurance companies deploy NLP-driven chatbots and virtual assistants on their websites, mobile apps, and customer portals to respond to customer queries, provide policy information, and assist with various tasks, such as policy renewals and claims submissions.

NLP-driven virtual assistants can guide policyholders through the claims process, helping them complete claim forms and providing updates on claim status. This improves the efficiency of claims processing and reduces the burden on employees.

It can also be used by clients to inquire about policy details, coverage, premiums and other information. Thanks to the translation options, NLP can facilitate multilingual customer support, allowing customers to use chatbots no matter the language they use.

Virtual assistants can also interpret and simplify complex policy documents, making them more understandable to customers, which enhances transparency and reduces confusion.

Another great example of the use of NLP-driven virtual assistants is that they help in handling customer complaints, escalating them to humans when required and tracking the resolution progress.

All of those use cases facilitate better customer service and retention.

Risk assessment reinvented: NLP’s role in policy underwriting

When it comes to NLP’s role in policy underwriting, it can be used to extract and analyse information from a wide range of unstructured data sources, including news articles, social media, medical records, and customer correspondence.

This data enrichment process provides insurers with a more comprehensive view of the potential policyholder, enabling more accurate risk assessment.

Using NLP techniques we can create detailed risk profiles for applicants by assessing their historical data, behaviour, and public sentiment. This helps underwriters make informed decisions about policy and pricing.

Automation of the collection of data from various sources reduces the administrative burden on the underwriter and ensures a more complete assessment of risk factors, further improving the way the insurance sector works.

Want to know more about NLP? Take a look at the related articles:

• How is Natural Language Processing (NLP) used in business?
• How is NLP transforming finance, FinTech, and banking?
• How do NLP and IDP address business challenges?

Sentiment analysis for customer feedback

Sentiment analysis is a very new and hot subject, allowing companies to analyse and understand customer feedback to improve their operations.

NLP-driven tools allow for automatic data collection from various sources (feedback forms, surveys), processing and sentiment classification, and categorisation of feedback into various sentiment levels.

By analysing historical sentiment data, insurance companies can recognise trends and patterns in customer feedback, which can reveal recurrent issues or changing customer preferences.

Based on sentiment analysis results, insurance companies can make data-driven decisions to enhance customer service, streamline processes, or improve policy offerings.

Sentiment analysis can also serve as an early warning system for customer satisfaction issues. When negative sentiments spike, insurers can take immediate actions to address the problems and prevent customer churn.

NLP for personalised policy recommendations

NLP is also employed to provide personalised policy recommendations, which enhance customer experience by tailoring insurance coverage to the specific needs of individuals.

It serves in the collection of data and its analysis, customer profiling, assessment of the level of risk associated with each customer and the recommendation of suitable insurance policies.

NLP allows also for customisation by adjusting coverage levels, deductibles and other features to fit clients’ budgets and preferences.

How insurers win with NLP integration: the immediate benefits

There is no doubt insurers win with NLP integration and are taking advantage of this new advancement in technology. NLP tools improve organisations’ efficiency, customer service and decision-making processes and reduce the company’s costs.

Predicting the next evolution of NLP in insurance

Although predicting the future is never easy, we can tell which of the uses of NLP in the insurance industry will be further developed in the months and years to come.

The most important of them include:

• advanced sentiment analysis, allowing not just to understand customers’ sentiments but also to predict them,
• further development of virtual assistants and chatbots, which will be able to conduct more complex conversations with customers,
• behavioural analytics, which will allow insurers to gain insights into customer preferences and needs,
• real-time risk assessment by analysing social media, news and other data sources to adjust policy pricing and coverage dynamically, based on emerging risks.

All of those advancements will help insurers stay competitive, improve customer satisfaction and effectively mitigate risk.

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James’ presentation was a fascinating story on the past, present and future of the data standards in the London insurance industry. Let us take you through it, starting from a quick history lesson.

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Data Standards in the 20th century

The end of the past century is when data standardisation started to take off. Until 1970s, some standards were there, but they tended to be multiplied. For example, Lloyd’s marine insurance had slightly different variations than non-marine; as did aviation. The eighties brought more standardisation.

As James said, “In 1986 data standards already started in the London Market. I was working for a small underwriter. As the losses and claims came in, we all filled them through the same standard form: pink was property, blue was marine and yellow was liability. Capturing the same data is still a data standard.”

In 2000, the Electronic Placing Support (EPS), an early e-trading solution, was introduced. “It was green screen, it was torturous, it was ahead of its time. It was painful, it was forward thinking, and it was unwanted. It was a huge amount of re-keying, the technology was not stable, integration wasn’t even a word. Few people were actually using technology.” – James recalled – “But it did open my eyes – and I’m sure many others – to how things could move forward.”

The beginning of the 21st century brought further improvements to the standards with the LMP 2001 which created the first London Market Processing slip. New versions of the slips were launched until the Market Reform Contract (MRC) in 2007.

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All of these slip versions, James argued, were ultimately the way of capturing and standardising data. Coming up with these standards meant companies didn’t have to create special processes for different classes. “We’ve now started generating standards – standard contracts, standard referencing. Excellent. Now we’re starting with the IT catching on with us.” James stated.

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Is the London Market capable of change?

In his presentation, James addressed the argument posed by some that the London Market wasn’t capable of change. He pointed out a few examples that show major changes that the industry has gone through:

• changing from the old school slip format to the MRC format with 100+ thousand policies across hundreds of firms, classes and other areas moving to a standardised format in 12 months
• changing the way premiums were calculated with 300 brokers getting rid of physical paper and starting to send data and pdfs
• changing the way claims were handled and processed with ECF and data

The industry has had many successes, but what it tends to forget about, James argued, is the failures.

We failed miserably…

“Yes, I have just used the magic word ‘failure’. I’m perfectly happy to stand here and say that we failed miserably on some of these things.” admitted James. ”Why? Because we didn’t bother asking the end user.”, James explained discussing examples of things which could have gone better. For example, the DDM system where brokers were keying the same data 5 times, because they hadn’t been included in the design of the system.

Some systems were good, but suffered from poor buy in. “Why didn’t they get embraced? Because somebody came along and said ‘here’s a new shiny thing”, James argued.

Along came further projects, like the 2010 Future Process, the 2016 CSRP and the 2023 Blueprint2 standard.

The curse of the legacy systems

“Somebody once said to me ‘we’re trying to play mp3s on a gramophone’. Ultimately what we’re trying to do is use XML messaging and APIs but underneath it is the old COBAL code mainframe that has been in place for around 40 years. It went live in 1986, same time I did – I’ve got old and creaky and so have those systems”, commented James.

He argued the market had a habit of being reactive rather than proactive, pointing out that change normally happens as a result of regulatory imperative.

What do the successes and failures prove?

The victorious and failed attempts at data standardisation have proven two things, James argued.

First, things fail without long term commitment and market buy in.

Second, there are huge business benefits to modernisation. “By bringing it all to the same slip, we didn’t have to have all these specialists, we could be crossing over. That’s an operational efficiency – everybody using the same standard means we didn’t need to set standards for systems. We proved we can do it if we bother putting our minds to it”, James said.

Remember this thing called ‘the pandemic’?

What did the Covid-19 pandemic do to the industry?

Two things:

1. people had to switch to using digital systems which may have not been as efficient as they wanted but the systems worked, and
2. communication became much easier with people moving to videoconferencing.

James commented: “The brokers and underwriters are now sitting at home thinking ‘how am I going to get my nice rubber stamp on this contract? I can’t. So, hang on, they forced me to do it through this system… and it really works, alright, maybe not as efficiently and effectively as we really want, but it works. (…) 18 months on there’s a dramatic shift after the country shutting down and there’s us emerging, blinking into the light and… arise Blueprint Two.”

Blueprint Two – what is it?

Blueprint Two is a programme aimed at digitalising the London insurance market to make it better, faster and cheaper. It’s about transforming the journey of placing risk and making claims for open market and delegated authority business. The programme is set to be delivered in multiple phases, the first two phases start next year.

In phase one central services will be moved to a new single digital platform and processing services for open market and delegated authority business. All market firms must adopt phase one by 1 July 2024, but change will be limited.

Phase two, starting from September 2024, will offer a set of services that fully utilise the new digital processing platform. Market participants can move from phase one to phase two at a time that is right for them.

Blueprint Two – components and phasing

James then went into the process for Blueprint Two in a bit more detail. First is the data definition and the Core Data Record (CDR) (now on version 3.2). Then, gathering data with a mechanism called the Market Reform Contract version 3 (MRC v3) and other slightly more technologically advanced solutions.

Work is now progressing on who’s going to provide the data – the phase called the ‘Process, Roles and Responsibilities’– and as of 11 October 23 the work pivoted towards the Claims.

On 1st July 2024, the old green screen platforms are going to be switched off and everyone will be moving across to new replacements – IPOS and ICOS and a gateway called IROS, as mentioned in the earlier section. Following this, in September, brokers and carriers should begin to have the ability to achieve full integration with messaging.

A goodbye to sellotape, string and chewing gum

James said he was optimistic that things would be done right this time because Lloyds and the company market have come together and are getting their processes and data requirements closer together which should result in scale and operational efficiency.

Innovation doesn’t stop: it’s change, it’s movement, it’s evolution

James brought up the question of ‘is going to be easy?’ and admitted: “No, absolutely not. It’s going to be really difficult. And rather annoyingly it’s going to cost money. But we’re not giving the market the choice, by turning off the old stuff.” He continued “People do know about standards; we’ve been doing them for years. We’ve just not necessarily done them technologically wise. If we’re going to take things away, it’s going to be difficult, it’s going to take time, there will be errors, it will cost, but it’s the right thing to do.” James concluded.

Further resources:

• www.liiba.co.uk
• https://lmg.london/data-council/

An interview with James Livett

Following James’ presentation on the evolution of data standards in the insurance industry, we asked him a few more follow-up questions revolving around top priorities and changes needed in the industry:

Future Processing: What is the no 1 priority for the insurance industry/London Market right now and why?

James Livett: LIIBA have just asked its members this question…and the general themes have been “Modernisation/Change” and “Cost”. A third is “Staff & Training”. Oddly I see them as all related. Blueprint 2 and other modernisations challenges are going to cost money and they will need skilled staff or staff training. A bit of a long-winded answer but ultimately you cannot deliver change or progress without adequate funding and good staff.

FP: If you had a magic wand and you could change the industry in any way, what would you do and why?

JL: I have one on my desk….it does not work! If it did, I would give everyone an understanding of what everyone else does. So many mistakes are made because no one considers the other parties in the chain.

FP: How do you see the Insurance Industry evolving beyond the Blueprint Two programme?

JL: BP2 is just the start. Change is constant, but one thing I see is that by gathering standardised data we open ourselves up to a greater level of analysis that can provide greater understanding of our Client’s need. Improved products and better risk management. This can only be achieved once we have the data.

FP: What role do you see for vendors in the digitalisation of the insurance industry and in the data standards drive?

JL: Vendors are absolutely central and essential to the delivery of all technological change. If the vendors are not at the heart of the move to digitisation it will never happen. It is incumbent on the market to ensure that they have a clear understanding of the desired outcomes but once done it is their skill and expertise that will actually deliver the ability to achieve the outcomes.

IT Insights: InsurTalk

Learn more about the latest trends, challenges that leaders face and the future of insurance.

Check out our interview series

Before we look at the insurance industry in detail, let’s look for a moment at digital transformation.

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Digital transformation encompasses various aspects, including technology adoption (AI, IoT), processes optimisation, enhancement of customer experience, data-driven decision making, organisational culture and change, as well as agile and flexible operations.

The goal of digital transformation is to create a digitally mature organisation that leverages technology to drive growth, improve competitiveness, and deliver value to stakeholders. It is a strategic on-going initiative that goes beyond implementing isolated digital solutions and requires a holistic approach that considers technology, processes, people, and culture.

Digital transformation in the insurance sector: from policies to pixels

Over the years, the insurance sector has undergone a significant digital transformation, shifting from traditional paper-based processes to digital platforms and technologies.

The first digitalisation efforts started in the 1980s and 1990s, when insurance companies began digitising their processes by adopting computer systems for policy management, claims processing, and customer data management. This marked the initial steps towards automating manual tasks and improving operational efficiency.

The next big step ahead happened in the late 1990s and early 2000s with the rise of the internet, when insurance companies started offering online platforms for customers to purchase policies directly. This shift allowed customers to compare plans, receive quotes, and complete transactions digitally, reducing the reliance on traditional agent-based sales.

With the increasing popularity of smartphones, insurance companies developed mobile applications to provide customers with convenient access to policy information, claims filing, and customer support. Today mobile apps enable policyholders to manage their policies, view coverage details, and file claims from their devices.

All of those processes happened over years and insurance companies were sometimes slow to adopt and embrace them. Yet the current digital transformation pushes them even further by unlocking new potential and giving access to new technologies in a more rapid way:

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Today, insurance companies use data analysis tools, telematics and usage-based insurance, all to streamline operations, enhance efficiency, improve risk assessment, and deliver a better customer experience. Insurers continue to explore emerging technologies and trends to stay competitive in a digitally-driven marketplace.

The potential and benefits of digital technologies in the insurance industry

Digital technologies have immense potential to transform the insurance industry and bring significant benefits for insurers, customers, and other stakeholders.

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Some key areas where digital technologies can have a positive impact include:

1. Enhanced Customer Experience

Digital technologies enable insurers to offer a seamless and personalised customer experience. Insurers can leverage data analytics, AI-powered chatbots, and self-service portals to provide 24/7 customer support, faster policy issuance, convenient claims processing, and personalised product recommendations.

2. Improved Underwriting and Risk Assessment

Digital technologies allow insurers to gather and analyse vast amounts of data from various sources, including social media, wearables, and telematics.

This data can be used to assess risks more accurately, develop personalised policies, and price premiums based on individual behaviour and usage patterns.

3. Efficient Claims Processing

Automation and digitisation streamline the claims process, reducing paperwork, manual interventions, and processing times. Technologies such as optical character recognition (OCR), image analysis, and automated workflows can expedite claims handling, improve accuracy, and reduce fraudulent claims.

4. Advanced Analytics and Predictive Modelling

Data analytics, machine learning, and predictive modelling help insurers gain valuable insights into customer behaviour, market trends, and risk patterns. These insights can drive more informed decision-making, enable proactive risk management, and support the development of innovative insurance products.

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5. Fraud Detection and Prevention

Digital technologies enable insurers to implement advanced fraud detection systems. AI algorithms can analyse data patterns and detect anomalies that indicate potential fraudulent activities, helping insurers mitigate risks and protect themselves and their customers from fraudulent claims.

6. Telematics and Usage-Based Insurance

Telematics technology, including GPS and sensors, allows insurers to collect real-time data on driving behaviour, enabling usage-based insurance models. Insurers can offer personalised premiums based on actual usage, safe driving habits, and other factors, incentivising policyholders to adopt safer behaviours.

7. Insurtech Collaboration

Insurtech startups bring innovative technologies and ideas to the insurance industry. Collaboration between traditional insurers and insurtech companies can drive digital innovation, improve operational efficiency, and deliver new products and services that cater to evolving customer needs.

8. Blockchain for Trust and Efficiency

Blockchain solution offers transparency, security, and trust in insurance transactions. It can streamline processes such as claims settlement, contract management, and policy verification by providing a decentralised and immutable ledger that all parties can access and trust.

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9. Risk Prevention and Mitigation

Digital technologies enable insurers to provide risk prevention and mitigation services to their customers.

For example, insurers can partner with IoT device manufacturers to offer smart home security solutions or collaborate with health and wellness apps to promote healthier lifestyles and reduce healthcare risks.

10. Data-driven Decision Making

Digital technologies provide insurers with access to vast amounts of data, enabling data-driven decision-making across all areas of the business.

Insights from data analytics help insurers identify market trends, optimise product portfolios, improve operational efficiency, and manage risks effectively.

11. Cost reduction

By minimising manual processes and eliminating paper-based workflows, insurers can significantly reduce administrative costs. Digital channels and self-service options also reduce the need for extensive agent networks, resulting in cost savings.

12. Innovation and product development

Insurers can leverage emerging technologies such as AI&ML, IoT, blockchain, and data analytics to develop new products and services.

These innovations may include usage-based insurance, on-demand coverage, digital platforms for policy comparison, and personalised offerings tailored to specific customer needs.

The potential of digital technologies in the insurance industry is vast and continually evolving. Insurers that embrace digital transformation and effectively harness these technologies can gain a competitive edge, deliver better customer experiences, and drive innovation in the insurance market.

The rise of insurtech: a new era for the insurance

The rise of Insurtech, which stands for insurance technology, has ushered in a new era for the insurance industry.

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Here are some key aspects of the Insurtech movement:

Innovation in product offerings

Insurtech startups have introduced innovative insurance products and services that cater to changing customer needs and preferences. These products often leverage emerging technologies such as AI, blockchain, and data analytics to provide more personalised coverage, on-demand policies, and usage-based insurance options.

Improved customer experience

Insurtech companies prioritise delivering a seamless and user-friendly customer experience. They leverage digital platforms, mobile apps, and self-service portals to simplify the insurance process, enable easy policy management, offer transparent pricing, and provide quick and efficient claims processing.

Insurtech firms often emphasise customer-centricity and aim to engage customers through intuitive interfaces and personalised interactions.

Data analytics and risk assessment

Insurtech companies leverage advanced data analytics techniques to gather, analyse, and interpret large volumes of data from various sources. This enables them to assess risks more accurately, develop innovative underwriting models, and provide personalised pricing based on individual behaviour and usage patterns.

Insurtech firms often rely on real-time data and predictive analytics to enhance risk assessment and mitigation strategies.

Automation and operational efficiency

Insurtech startups leverage automation technologies to streamline and digitise insurance processes, eliminating manual paperwork and reducing administrative burdens.

Robotic process automation (RPA) and AI-powered chatbots are used for tasks like policy issuance, claims processing, customer support, and data entry. This automation enhances operational efficiency, reduces costs, and allows employees to focus on higher-value activities.

Insurtech ecosystems and partnerships

Insurtech companies often collaborate with traditional insurance carriers, technology firms, and other players in the insurance ecosystem. These partnerships facilitate knowledge sharing, access to resources, and the integration of innovative technologies.

Insurtech startups also collaborate with data providers, IoT device manufacturers, and other industry stakeholders to access relevant data sources and enhance their offerings.

Disruption and market competition

Insurtech has introduced competition and disruption to the traditional insurance landscape. Insurtech startups are challenging established players with their agility, customer-centric approach, and innovative business models.

This has led to increased market competition, prompting traditional insurers to embrace digital transformation and explore partnerships or investments in Insurtech ventures.

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Insurtech regulatory environment

Regulators are adapting to the evolving Insurtech landscape by introducing frameworks and guidelines to address regulatory challenges and ensure consumer protection.

Regulatory sandboxes, which provide a controlled environment for testing innovative insurance solutions, have emerged in several jurisdictions, allowing Insurtech firms to experiment with their products and services within defined regulatory boundaries.

Open insurance and API integration

Insurtech promotes the concept of open insurance, encouraging the use of application programming interfaces (APIs) to enable seamless integration and collaboration between insurance companies, Insurtech startups, and other digital platforms. This allows for data sharing, faster product development, and enhanced customer experiences through integrated services.

The rise of Insurtech has brought forth a wave of innovation and digital transformation in the insurance industry. Traditional insurers are increasingly adopting Insurtech practices and partnering with Insurtech startups to stay competitive in the evolving digital landscape.

The continued growth of Insurtech is expected to shape the future of insurance, driving customer-centricity, operational efficiency, and technological advancements.

Challenges of digitalisation in insurance

While digitalisation brings numerous benefits to the insurance industry, it also presents several challenges that insurers must navigate.

The key ones include:

1. Legacy systems and infrastructure – many insurance companies have complex legacy systems and infrastructure that may not be compatible with modern digital technologies. Upgrading or integrating these systems is a challenging operation that requires time, effort, and great planning. Legacy systems can hinder the agility and flexibility required for effective digital transformation.
2. Data management and privacy – digitalisation generates vast amounts of data, and insurers must effectively manage, store, and secure this data. Ensuring data privacy and compliance with regulations, such as the General Data Protection Regulation (GDPR), is crucial. Data breaches and mishandling of customer data can damage an insurer’s reputation and result in legal and financial consequences.
3. Change management and workforce adaptability – implementing digital transformation requires a cultural shift within the organisation and the adaptation of employees to new processes and technologies. Resistance to change and lack of digital skills can impede successful digitalisation efforts. Insurers need to invest in change management strategies, training programs, and hiring or upskilling employees with digital competencies.
4. Cybersecurity risks – as insurers adopt digital technologies, they become more vulnerable to cyber threats. Cyberattacks, data breaches, and ransomware attacks can lead to significant financial losses, reputational damage, and customer distrust. To mitigate these risks insurance companies should invest in cybersecurity measures and think about them as an inseparable part of their operations.
5. Customer expectations and experience – digitalisation, while improving customer experience, also raises expectations. Today customers expect seamless digital interactions, personalised services, and real-time access to information. Meeting these expectations requires insurers to invest in user-friendly interfaces, omnichannel capabilities, and responsive customer support. Failing to deliver a satisfactory digital experience can lead to customer churn.
6. Regulatory and compliance requirements – insurance is a highly regulated industry, and digitalisation adds complexity to compliance efforts. Insurers must navigate regulatory frameworks and ensure that digital processes comply with legal requirements. Staying abreast of regulatory changes and adapting digital systems accordingly can be challenging.
7. Integration and collaboration – digitalisation often involves integrating various systems, platforms, and data sources. Insurers may face challenges when integrating with external partners, such as Insurtech startups, third-party vendors, or data providers. Ensuring seamless data exchange, interoperability, and maintaining security standards across these integrations is a complex and challenging task, which should be undertaken carefully and with expertise by experienced professionals.
8. Overcoming industry inertia – the insurance industry, known for its traditional practices and risk-averse nature, can be resistant to change. Encouraging widespread adoption of digital transformation initiatives and driving innovation may require overcoming organisational inertia, aligning stakeholders, and fostering a culture of innovation within the industry.

While these challenges exist, they can be mitigated through careful planning, investment in technology and infrastructure, collaboration with insurtech partners, effective change management, and prioritising cybersecurity and data privacy.

Successful digitalisation efforts require a holistic approach that addresses these challenges while keeping customer needs and market trends in focus.

Companies that went through successful insurance transformation: examples

Several companies in the insurance industry have successfully undergone digital transformation to stay competitive and deliver innovative solutions.

Here are a few examples:

1. AXA – a multinational insurance company that has embarked on a digital transformation journey to enhance its customer experience and operational efficiency. It has developed digital platforms and mobile apps to enable customers to manage policies, file claims, and access insurance services. AXA has also partnered with Insurtech startups and invested in digital innovation labs to drive new ideas and technologies. Its separate division, AXA XL, embrace usage of new technologies and platforms.
2. Allianz – one of the largest insurance companies globally that has embraced digitalisation to enhance its customer offerings and improve operational efficiency. It has invested in digital platforms, data analytics, and automation to streamline underwriting, claims processing, and customer interactions. Allianz has also explored emerging technologies such as blockchain for secure and transparent transactions.
3. Zurich Insurance has undergone digital transformation to strengthen its market position and improve customer engagement. It has adopted digital platforms and automation tools for policy administration, claims processing, and risk assessment. Zurich Insurance also collaborates with Insurtech startups through its innovation lab, seeking new technologies and business models to drive innovation.

These companies serve as examples of successful digital transformation in the insurance industry. They have leveraged technology, embraced innovation, and prioritised customer-centric approaches to thrive in the digital era.

The future of insurance: trends shaping the digital transformation

The future of insurance is being shaped by several key trends that are driving the digital transformation of the industry. These trends are reshaping the way insurance products are developed, distributed, and serviced, and they have a significant impact on customer expectations and the overall insurance landscape.

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1. Personalisation and Customisation

Customers increasingly expect personalised insurance products that cater to their unique needs and circumstances. Digital technologies enable insurers to gather and analyse vast amounts of data to offer personalised coverage, pricing, and risk management solutions.

Insurers are leveraging technologies such as data analytics, AI, and machine learning to assess risks, create tailored policies or services.

2. Usage-Based Insurance

Usage-based insurance (UBI) is gaining popularity as insurers leverage telematics, IoT devices, and other data sources to assess risks based on actual usage patterns.

UBI allows for more accurate risk assessment and pricing, as premiums are determined by the individual’s behaviour, driving habits, or usage of insured assets. This trend promotes fairness, encourages safer behaviour, and enhances customer engagement.

3. Insurtech and Collaboration

Insurtech startups continue to disrupt the insurance industry by introducing innovative business models, technologies, and customer-centric solutions.

Collaboration between traditional insurers and Insurtech companies is becoming more prevalent, allowing insurers to tap into the agility and technological expertise of startups. Partnerships and investments in Insurtech ventures enable traditional insurers to accelerate their digital transformation efforts and drive innovation.

4. Digital Platforms and Ecosystems

Digital platforms are playing a significant role in reshaping insurance distribution and customer interactions. Insurers are leveraging digital platforms to offer self-service options, enable seamless policy management, and provide value-added services beyond traditional insurance coverage.

Additionally, insurance ecosystems are emerging, where insurers collaborate with other industry players, such as Insurtech startups, healthcare providers, and technology companies, to offer integrated and comprehensive solutions to customers.

5. Customer Experience and Engagement

Customer expectations are evolving, and insurers are focusing on enhancing the overall customer experience. Digital technologies enable insurers to offer user-friendly interfaces, personalised interactions, and convenient self-service options.

Insurers are investing in customer-centric digital strategies, including mobile apps, chatbots, and AI-powered virtual assistants, to provide quick responses, streamline processes, and deliver personalised recommendations or services.

6. Advanced Analytics and AI

Data analytics and AI play a crucial role in the future of insurance. Insurers are using advanced analytics to gain insights from large volumes of data, identify patterns, and make data-driven decisions.

AI-powered algorithms are being deployed for various tasks, including underwriting, claims processing, fraud detection, and customer service. These technologies improve efficiency, accuracy, and speed, while also enabling insurers to offer proactive risk management and personalised experiences.

7. Cybersecurity and Data Privacy

With the increasing reliance on digital technologies and the growing threat of cyberattacks, cybersecurity and data privacy are critical concerns for insurers. The future of insurance requires robust security measures, proactive risk management, and compliance with data protection regulations.

Insurers must invest in cybersecurity technologies, conduct regular audits, and prioritise data privacy to build trust with customers and protect sensitive information:

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8. On-demand insurance for gig economy

Traditional insurance products often did not adequately address the specific needs of gig workers (those with temporary or freelance work arrangements) which led to the emergence of Insurtech companies aiming to offer more flexible and tailored insurance solutions, cost-effective and allowing the specific clients to find peace of mind.

9. Parametric insurance

Parametric insurance is a type of coverage that automatically pays out a predetermined amount based on a specific event or parameter, rather than traditional indemnity-based insurance which reimburses the actual loss incurred. This approach also proves to be time-efficient and flexible.

These trends highlight the transformative impact of digital technologies on the insurance industry. To stay competitive and meet evolving customer expectations, insurers need to embrace these trends, invest in digital capabilities, and foster a culture of innovation.

The future of insurance lies in leveraging technology to create personalised experiences, offer tailored products, and build customer-centric ecosystems that go beyond traditional insurance coverage.

Digital transformation strategy: the roadmap for improvement

Developing a comprehensive digital transformation strategy is crucial for organisations aiming to improve their operations, enhance customer experiences, and stay competitive in the digital age. Such a strategy should contain clearly defined visions and objectives, prioritisation of areas of transformation, a roadmap that outlines the steps and timeline, as well as the plan for monitoring the project and communication about it across the organisation.

Remember that digital transformation is not a one-time event, but an ongoing journey. It requires commitment, flexibility, and a willingness to embrace change.

A great way of starting your digital transformation process is to collaborate with an external partner, experienced in working with insurance companies keen to embrace this new world of innovation. Our team at Future Processing is delivering such projects for high profile organisations within insurance industry, so we will be happy to look into your case and help you make the most of digital transformation!

Digital Transformation for the Insurance Sector

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