Jerry Wallis, Head of Industry Strategy, SS&C Blue Prism 

The insurance sector has faced a perfect storm of events these past few years. The covid-19 pandemic accelerated digital transformation around the world – which had the added effect of contributing to increased customer expectations and a surge in competitive pressures.  In addition, many long-established insurers have to maintain legacy systems that support books of insurance that can be years, even decades old – and that cannot easily and cost-effectively be replaced.  Having customer data stored in multiple different systems makes it very difficult for an insurer to build a single 360-degree view of a customer, to serve them better and to sell them more. 

While businesses in various industries have sped up their digital transformations to meet the demands of an online world, the tie to these legacy silos has meant the insurance industry has historically been slow to move into a truly digital way or working.  . The average underwriter, for instance, continues to spend more than 50% of their workday on repetitive tasks.

The sector is under tremendous pressure to process information faster, better, and cheaper to meet the changing needs of today’s customers and secure long-term competitiveness. The adoption of advanced technologies, namely intelligent automation (IA), is helping insurers overcome this challenge by changing how the industry operates across every aspect of the value chain – from product development, underwriting, and policy management, to claims and other processes.

This piece will explore how intelligent automation is launching a new era of improved productivity in the insurance industry.

Digital transformation and IA are imperative to insurers’ future prosperity

The rise of intelligent automation has brought about a new era of possibilities for the insurance industry, with an impressive range of benefits. The introduction of IA and its respective technologies into an insurance firm represents the future of what can become a much more technologically advanced sector. This is particularly important as the industry is under increasing pressure to not only reduce costs but to also maintain, and take steps to improve, customer satisfaction.

Intelligent automation adoption can help resolve this by unifying disparate silos of data, presenting users with a single, digitally capable view of customers, thus giving them the time they need to focus on complex customer cases and the ability to utilize IA to deliver superior, bespoke customer service. IA is a combination of components, including artificial intelligence (AI), robotic process automation (RPA), business process management and other complementary technologies that enable companies to advance workflows and streamline end-to-end processes.   

Digital labour helps workers by automating repetitive and mundane tasks, freeing people from repetitive and time-consuming work. Digital workers connect to legacy or modern applications to automate business processes through a variety of automation techniques.  .

Intelligent document processing allows insurance firms to process vast amounts of data with minimal human intervention at an over 98% rate of accuracy. This replaces laborious and error-prone data entry, which is not only slow but creates an inefficient and costly domino effect when information is input incorrectly. Artificial intelligence components can then use this information to provide valuable insights, predictive analytics and modeling regarding customers and their policies, and suggestions for optimizing processes.

Business process management provides digital oversight, enabling employees to know exactly where in the workflow items are and what needs to be completed to get tasks to completion. Intelligent process mining identifies areas that would benefit from automation, transforming the end-to-end processing of work.

Overall, these and other advanced IA technologies work together to streamline business processes, reduce operational costs, and improve the accuracy and speed of services. Using IA delivers key benefits for insurance firms, which include:

Faster claims processing – IA can automate many of the tasks involved in processing insurance claims. For example, it can read and analyze claims documents (including handwritten documents), determine whether a claim is valid, and calculate the amount of compensation owed. This can help insurers process claims more quickly, reducing the time it takes for customers to receive their payouts.

Improved customer service – The introduction of chatbots powered by natural language processing can answer customer queries and resolve simple issues. This frees up customer service representatives to focus on more complex issues, improving overall service levels. Predictive analytics help workers identify customer needs and preferences to better personalize products and services. Automated notifications can be used to notify customers of policy renewals, claim status updates, and other important information. This can help improve customer satisfaction by keeping them informed in real time.

Better risk assessment – By analyzing large amounts of data, the AI features of intelligent automation can identify patterns and make predictions about future events as well as customers. This can help insurers to price policies more accurately and avoid underwriting risks that may otherwise be too high.

More efficient underwriting – By automating tasks involved in underwriting policies, insurers can improve efficiencies and productivity. For example, IA can analyze customer data to determine their risk profile, check for policy compliance, and generate policy documents. BPM ensures the underwriting process moves along to completion efficiently. This efficiency reduces the time and costs involved in underwriting policies, allowing insurers to process more policies in less time.

Enhanced fraud detection – By analyzing large amounts of data, intelligent automation’s AI capabilities can identify patterns and anomalies that may indicate fraudulent behavior. This can help insurers detect and prevent fraud before it occurs, reducing the amount of money lost to fraud.

Insurers can build thriving workforces in the age of IA                                     

Another benefit of introducing IA is that insurers can develop and improve their workers’ skillsets to match the needs of an increasingly digitalized world. Insurers can also recruit new talent that is interested in learning about advanced technology. Team members that were once spending their days completing repetitive, time-consuming tasks, can be trained in the latest IA technologies to establish them as customer-focused underwriters.

Improvements in efficiency, skillsets, and recruitment help insurers build stronger workforces.

Intelligent automation is for all sector players

Don’t make the mistake of assuming the benefits of IA are confined to the big-league, multinational, insurance players. Intelligent automation is for all in the industry– from agencies and specialty insurers to regional insurers and – yes – multinationals.

For small and mid-sized players, intelligent automation presents an opportunity to overcome staffing and scale challenges to effectively compete in the marketplace and, thus, optimize revenue. Not only does IA improve efficiencies but it can also help insurers innovate and develop new products and services more quickly and effectively, allowing them to stay ahead of the competition and meet the evolving needs of their customers. But to be successful, businesses need to make digital transformation a strategic priority. For those that do, they will prove their ability to adapt to a rapidly evolving market and ensure their future growth.

“The rise and introduction of intelligent automation has brought about a new era of possibilities for the insurance industry. The gradual adoption of IA technologies into an insurance firm represents the future of what can become a much more technologically advanced sector. The adoption of AI and IA technologies is help insurers overcome this challenge by changing how the industry operates across every aspect of the value chain – from product development, underwriting and policy management, to claims and other processes.

“IA is a combination of components, including artificial intelligence (AI), robotic process automation (RPA), business process management and other complementary technologies that enable companies to advance workflows and streamline end-to-end processes. Digital labour helps workers by automating repetitive and mundane tasks, freeing people from repetitive and time-consuming work. Digital workers connect to legacy or modern applications to automate business processes through a variety of automation techniques.  For instance, intelligent document processing, improved customer service via chatbots, better risk assessment with AI features of automation identifying patterns and predictions, enhanced fraud detection and efficient underwriting. AI and IA will redefine processes within the insurance industry, but also help insurers innovate and develop new products and services quickly and effectively, putting them ahead of competitors and allowing them to and meet the evolving needs of their customers.”

Getting back to engineering fundamentals with driver-in-the-loop simulation

Just suppose for a second that Frank Whittle had never been able to tinker with his earliest ideas about the turbojet engine. Or that Tim Berners-Lee had run out of time before developing what went on to become the World Wide Web.

History is surely littered with great inventions that never happened. Of course, we’ll never know what they were, but it’s fair to assume there have been thousands of them. Imagine all the bright ideas that might have been, were it not for time pressures, budgetary limits, technological constraints or logistical issues.

Right now, there are few sectors more turbulent than the automotive industry. The full-scale charge towards electrification (no pun intended) has forced manufacturers to re-evaluate almost every area of the automobile. From the vehicle dynamics impact of heavier powertrains and low-slung battery masses, through to the UI/UX challenges of helping the drivers manage their energy usage, almost every aspect of modern vehicle design changes in some way with an EV.

And that’s by no means the only challenge facing the automotive industry at the moment. Increasingly sophisticated ADAS, self-driving functions, greater demands for in-car connectivity and the move towards new business models, such as subscription services and shared mobility, are all vying for design resources too. Plus, the pressure is always on to shorten development cycles and reduce costs.

Although all this disruption has the potential to prompt innovation, it can also stifle it. With the stakes being so high, is it really worth gambling on the development of a novel solution when a well-proven one will do? What is the value proposition of introducing new technologies in the context of maintaining and elevating brand identity? These are the difficult questions facing automotive manufacturers worldwide.

Fortunately, cutting edge research and simulation technologies such as Driver-in-the-loop (DIL) simulation allow vehicle development engineers to shift the odds dramatically in their favour. With DIL simulation, for instance, a virtual prototype for proof of concept investigations can be put together in a matter of days at minimal cost, whereas a physical prototype can take six months and more than half a million pounds to build. Multiply that out across an entire prototype fleet, and it becomes easy to see why management might think twice before signing off a high-risk programme unless it can be supported by appropriately advanced tools and techniques.

DIL simulation largely removes the risks associated with these ‘what if?’ questions, while simultaneously expanding the sandbox. Imagine, for instance, that you were planning a new electric vehicle platform and pondering whether the dynamic benefits of running an individual motor for each wheel justified the additional packaging complexity. Or perhaps the outgoing model used MacPherson Strut suspension and you wanted to evaluate whether the cost of re-engineering it for a double wishbone setup would be justified.

DIL simulation provides the freedom to pose these questions, months ahead of any physical builds or testing. It also provides an ongoing benefit with total freedom to vary the test conditions combined with laboratory-calibre repeatability.

There are no logistical issues to worry about, either. With physical testing, there’s always a danger that you’ll arrive to discover that a winter testing  venue is experiencing a warm snap or that a desert proving ground is under flash flood advisory. Even if everything goes to plan, it takes a considerable amount of time and money to ship a fleet of prototype vehicles around the world. The environmental impact of doing so can’t be ignored either.

The same applies to off-line testing to a certain extent, but when it comes to vehicle attribute decisions, there’s no substitute for having a human driver (or occupant) inside the loop, actually interacting with and experiencing the vehicle. Human beings perceive things that might not be immediately apparent in numerical data. For instance, an ADAS system such as Lane Keep Assist may fulfil all its on-paper design criteria, but only a human driver can judge whether it feels too intrusive or too eager to intervene.  

But the benefits of placing a human in the loop go way beyond subjective assessment. When we are in a car, we are experiencing an on-going feedback loop with the vehicle; for example, each vehicle control input – say, a steering, throttle or brake correction – is a carefully calculated response from our brain, based on dozens of different vehicle stimuli combined together and interpreted by senses. Without those uniquely human interpretations and responses, it’s impossible to get truly representative inputs for, say, a test bench or an off-line vehicle simulation model. Fundamentally, the human is a vital part of the equation, even when it’s a matter of collecting purely objective, numerical data.

Returning to the ADAS example: an aggressive intervention from an ostensibly assistive system might inadvertently trigger a human driver to overcorrect, doing more harm than good. Another classic example is high performance optimization related to overall vehicle stability; pick a suspension and steering setup that’s too aggressive in terms of, say, yaw from steer response, and it may theoretically be better, but you risk creating a car that even a Formula 1 driver would struggle to control. It’s a matter of keeping engineering fundamentals in mind while being immersed in a large number of new automotive technologies – as well as a matter of having access to safe and robust exploratory tools such as DIL simulation.

A DIL simulator is a considerable investment, it’s true, but it’s one that pays for itself quite quickly. Continental, for example, has recently invested in an Ansible Motion Delta series S3 simulator, and predicts that it can save around 10,000 tyres and 100,000 test kilometres per year as a result. Savings for vehicle OEMs that currently run large prototype vehicle test fleets could be greater still.

It’s also worth noting that DIL simulators are becoming more accessible than ever. The Bay Zoltán Research Centre in Hungary recently began offering open access to another Delta series S3 simulator. Designed to be software-agnostic, their DIL simulator lab allows engineering teams to bring their own vehicle and environment models, created in virtually any major simulation environment. The Centre can even support customers who wish to build their own bespoke cabins to use on the motion system.

All of this means that it’s now easier than ever for engineers to delve into the important questions that surround modern vehicle design and development. Ultimately, DIL simulation and other cutting-edge tools provide the freedom to explore and innovate, by making use of advanced technologies that are commensurate with the advanced state of contemporary automobiles. In a world that’s all too frequently constrained by budgets or logistics or technology itself, it is nice to know that there are tools that enable engineers to focus on engineering once more.

Source: Finance Derivative

Adding efficiencies, automating processes and strengthening cybersecurity efforts: data engineering can be crucial in support scaling fintechs, says Krzysztof Michalik, Head of Delivery – FinTech Stream at STX Next.

In short, data engineering is the practice of designing and building systems for collecting, storing, and analysing data at scale. For modern fintechs, banks and financial institutions, it can play a key role in supporting their attempts to handle and make sense of growing volumes of sensitive, unstructured data. Data engineering provides the support and infrastructure necessary for fintechs to digitise their workflows and optimise business processes.

Meeting the highest security standards, processing and protecting large amounts of confidential data and ensuring service availability are all ongoing efforts. As data engineering-powered tools and technologies have become more accessible, organisations will increasingly rely on the insights they can produce for decision-making as well as lean on their capability to enhance a business’ agility, dynamism and profitability.

Data processing for valuable insights

Banks and financial institutions will frequently rely on innovative, high-end solutions that process and analyse financial data. Many fintech businesses act on this by accessing data platforms or services that can automate tedious processes, doing so quickly and in the process cutting out any potential for human error. This can then be applied to tasks like approvals or services like credit risk indicators, and can assist in identifying fraud patterns in real time to optimise lending operations.

Through processing large amounts of data, data engineering processes can support a business in identifying trends, making predictions and optimising operations to deliver better financial services to its customers. Consolidating data in real time to provide actionable insights for better decision-making can support the provision of high-end services.

Detecting irregular activity and financial crime

Data engineering can also power tools that support businesses in identifying and stopping financial crime. In many cases, businesses will implement systems with the ability to analyse, detect and highlight irregular activity, saving time and money whilst improving the accuracy of the process.

Preventing and detecting financial crime is one of the biggest challenges facing financial institutions globally. In response, businesses are increasingly looking to technology to keep pace with a growing list of regulations and protect sensitive customer data from new threats. As the fintech market evolves, so does the need for enhanced security and data protection.

To add to this, financial institutions are bearing the burden of processing more sensitive customer data than ever before and handling higher transaction volumes. All the while they are facing more sophisticated threats, while the fallout from attacks and data breaches in fines and reputational damage are arguably more significant.

Data engineering can also support regulation and compliance efforts. Implementing better security measures and protecting sensitive customer data through cloud-based data storage tools with built-in security and compliance features can be the difference when it comes to avoiding hefty fines. Simultaneously, data engineering can help improve data accessibility and reduce storage costs.

Data unification

Data unification tools can also be helpful in providing a business with digestible insights through collection and analysis of data from multiple different sources. This data is then presented via easy-to-understand dashboards that can help business support decision-making on wider business needs and highlighting new opportunities for growth. 

Data unification can also support decision-makers by providing a comprehensive view of investment opportunities. For example, it can help organisations to streamline the process of identifying and recommending different lending products, which will reduce overall risk exposure.

What’s next?

For businesses looking to steal an edge over their competitors, data engineering can play a crucial role in automating processes and efficiencies, but also in their security, compliance and anti-fraud efforts. The modern fintech should harness data engineering’s potential and reap its rewards.