How is data analytics expected to impact decision-making in the FinTech industry? 

How is data analytics expected to impact decision-making in the FinTech industry? 

Introducing this month’s question with insight into auto insurers, AI and data is Simon Axon, EMEA Financial Services Industry Consulting Director at Teradata. 

Simon Axon, EMEA Financial Services Industry Consulting Director at Teradata

Navigating a perilous road 

It is a tough time to be in auto insurance. Historically, insurers have often struggled to turn a profit on auto insurance policies, but post-pandemic economic conditions have only further undermined combined operating ratios. Supply chain issues, sticky inflation and the rising number of claims all continue to put upward pressure on operating costs. And premiums are not keeping pace. While motorist may have to live with premium increases of 16% or more this year, insurers are not daring to raise rates significantly due to the current cost-of-living crisis.  

To add to the trouble, the industry own very structure is changing. According to McKinsey projections, technological advancements are likely to significantly disrupt the auto insurance risk pool in the US by 2030, leaving traditional insurers with a shrinking share of value.  

Already, new technologies, including AI-driven opportunities, are opening the door to competitors from outside the sector who are enticing customers with innovative new products. These include: 

  • Big Tech players with their native expertise in data and their relentless drive to offer seamless, integrated, and high-value services to customers 
  • Auto manufacturers and brands, which are already using sensor data and their network of dealerships (for repairs and parts) to build stronger lifetime relationships with drivers 
  • Neo-banks and other emerging FinTech companies, such as Revolut in Ireland, which is offering discounted policies to safe drivers based on data collected from telematic devices 

Opportunities under the hood 

Modern vehicles are packed with sensors that not only provide continuous performance data but also offer insights into individual driver behaviour. But the ‘black boxes’ used by some insurers are becoming outdated in terms of the type and amount of data they can provide. There is already talk of vehicles self-reporting accidents direct to insurers. The opportunity—and the necessity—for insurers lies not only in using this data for personalised quotes and usage-based policies, but also in imagining a wide range of additional services tailored to customer segments of one.  

Last year, McKinsey outlined a hypothetical journey for a US auto insurance customer, envisioning real-time risk calculations and liability shifting among different insured parties based on autonomous and semi-autonomous driving, with second-by-second policy pricing. While these services are not yet available, the technology to make them achievable already exists. There is no reason why similar data and techniques could not be applied to reshape auto insurance norms and create innovative products. 

Pulling ahead with data in the driver’s seat 

If traditional auto insurers want to compete with the innovative companies entering the market, they must quickly accelerate their use of data for automation, Machine Learning, and AI. By collecting and analysing customer, telemetry and mobility data and leveraging analytic insights from the broader ecosystem of partners, insurers can better understand customer behaviour to improve customer engagement and create more value.  

To survive and thrive in the AI-driven decade ahead, insurers must transition from the traditional annual renewal and customer contact cycle to ‘Always-on,’ real-time relationships, continuously offering new products and services that create digital bonds with individuals at their point of need. Achieving this requires cultivating a data culture and investing in an enterprise-wide cloud analytics and data platform for AI that offers the speed and scalability to analyse billions of data points in real time. 

Angus Panton, Director of Banking and Financial Services at Expleo 

Angus Panton, Director of Banking and Financial Services at Expleo 

Data is at the heart of digital transformation. Recent years have seen a fundamental shift in the way organisations collect, store and process data, and the conclusions they draw from it. This has opened the door to a profound change in the industry: 

  • Automating processes and customer interactions  
  • Introducing digital business models  
  • Reducing operational costs  
  • Enabling greater personalisation of product portfolios  
  • Meeting regulatory requirements and helping to detect fraud  
  • Improving decision support for customers and employees 

Automated data analysis and processing is a key focus for banks and financial services companies: 

Research shows that 39% of BTI 2023 financial sector respondents expect to add Machine Learning (ML) and Artificial Intelligence (AI) to their organisation’s transformation plans over the next one to two years. 

Using data, not just collecting it  

Comprehensive data preparation and processing is fundamental to a business-driven data strategy. Unlike humans, machines leverage increasing amounts of information. Hence, the greater the information available, the more optimal the results. The data strategy that a bank implements should take this into account, as the BTI 20231 shows.  

Some of the BIGGEST CHALLENGES for the use of data in financial organisations: 

  • 64% – Keeping up to date 
  • 63% – Sharing of data between systems and departments 
  • 67% – Data accuracy 
  • 69% – Making full use of AI and machine learning 
  • 65% – Using data to create new revenue-generating services 
  • 63% – Data science / analytical skills 

AI and data analytics will fundamentally change the banking world of tomorrow 

One of the most important customer interaction points, and where AI becomes truly tangible to the customer, is the chatbot. Modern conversational chatbots will lead to a revolution in communication, as these machines differ from traditional FAQ-based bots in that customers can formulate their questions in descriptive terms. The AI-powered chatbot understands the query and responds accordingly. Thanks to a range of new off-the-shelf products, customised solutions can be developed quickly and efficiently using open-source technologies. Banking chatbots outperform human operators in terms of speed, precision, and seamless automation, driving enhanced efficiency and cost reduction. A new generation of bots is also capable of providing tailored financial advice. 

Case study 

We helped one of the world’s largest FinTechs build data models to interpret and present to a non-technical audience. To do this, Expleo built a data pipeline that uses Big Data systems to prepare, cleanse and transform data. It also enabled the automation of repeatable analysis and the creation of self-service tools for business users. 

Vinod Singh, CTO, Concirrus 

Vinod Singh, CTO, Concirrus 

Data analytics wields significant influence in shaping decisions within the FinTech and insurance sectors, revolutionising traditional approaches and fostering more informed strategies. In FinTech, this manifests through personalised financial guidance and enhanced fraud detection. Algorithms parse through extensive transaction histories, demographic data, and market trends to offer bespoke solutions such as loans, investments and insurance plans tailored to individual needs. Furthermore, analysis of past fraudulent activities enables the identification of patterns, bolstering security measures to safeguard user accounts. 

Similarly, in the insurance domain, data analytics is ushering in a paradigm shift from generic demographic-based policies to personalised coverage and pricing structures. Access to data streams from driving sensors, health wearables and sophisticated customer segmentation enables insurers to underwrite policies based on real-time behaviors and risks. This results in more precise premium calculations and incentives for clients to adopt safer lifestyles. Additionally, streamlined claims processing through automated decision trees and fraud analysis expedites customer service and enhances operational efficiency. 

Across banking, investing and insurance, data analytics complements human judgment by unveiling correlations and trends that might elude traditional intuition. Algorithms provide decision-makers with impartially surfaced evidence, empowering them to make more informed choices. Furthermore, analytics furnishes leaders with comprehensive dashboard visibility into performance metrics and operational health, facilitating proactive decision-making. 

As data analytics continues to evolve alongside the expansion of FinTech and insurance data reservoirs—fuelled by increased customer engagement with mobile apps, shared services, embedded sensors and the Internet of Things—the insights it offers are poised to become even more profound. Soon, leaders may rely on data scientists and Artificial Intelligence as much as human consultants to analyse past outcomes, simulate future scenarios and optimise executive decisions based on robust empirical evidence. Consequently, decisions informed by analytics stand to be more inclusive, equitable, personalized and strategically astute. 

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