A look into AI and ML trends in banking and insurance
The rise of intelligent finance
AI and ML, which have been used in the past to improve existing products and services through intelligent automation, have now been seized on by insurance service providers, financial organisations, and fintechs to create new value propositions driven by enhanced capabilities. These opportunities span across multiple functions and include the following benefits:
- improved targeting of products and personalisation of service offerings
- increased revenue and cross-selling through enhanced knowledge of the individual customer
- reduced risk, as well as the detection and reduction of fraud
- improved employee experience by freeing staff up from low-value work
- cost optimisation and improved efficiencies through increased productivity, which in turn drives improved margins in the business as a whole.
A focus on insight-driven customer experience
In other customer-facing industries, a recent imperative has been to achieve a single and unified view of the customer to enable the personalisation of products and services to specific customer demands.
Most financial services companies focus on keeping margins level during a time of cheap customer borrowing by establishing new products. During the pandemic, most organisations, regardless of industry, saw customers’ expectations increase significantly alongside a willingness to shop around.
Longstanding financial service providers have seen new and aggressive entrants targeting these more demanding customers with many products which couple the financial service with convenient and flexible solutions. Hyperscalers and other large and well-funded organisations are poised to enter the market.
These companies are now responding strongly to these challenges, using all means, including next-gen AI and ML solutions, to improve their service offerings and deliver efficiencies wherever feasible.
Addressing the risks and hurdles of AI and ML in the finance sector
As with any innovation, AI and ML come with some hurdles and impediments. With the rationale of AI and ML predicated on the input data, the rules and algorithmic processing and access to high-quality data is paramount. This is of even higher importance for organisations in which the input assumptions could have a catastrophically negative effect on the business in case of machine error.
Let’s look closely at some potential challenges associated with AI implementation:
- Tackling bias and fairness
There is a risk of minor errors combining to form market-wide risks and biases in credit analytics, for example, or in the assessment of risk premiums. These risks can be mitigated by the use of platforms which allow for real-time reporting and decision-making based on the most current view and the use of sustainable — and ideally exclusive and secure— data sources to train the analytical engines. These methods also require human monitoring and intervention to ensure the runaway does not occur.
- Navigating compliance challenges
Financial service companies are also subject to regulation, which tends to lag technological advancement. Regulations create an uncertain and inconsistent set of expectations and requirements for global organisations and generate demands which can evolve from negligible to complex in a short timeframe. This is especially true of data sharing between jurisdictions and between corporate and personal entities, an area of intense focus for AI and ML.
- Ethical considerations and human factors
The need for immediate, on-demand data at scale has seen the formation of digital platforms which harness AI- and ML-enabled products and services. It can result in superior relationships between buyers and suppliers, as seen in other customer-facing industries which have already focused on customer centricity first and product second. AI and ML should enhance workers, not replace them. Humans should retain control of decision-making, with AI and ML applications providing decision support and recommendations, as well as performing repetitive manual processing more efficiently and providing the user interface with improved management information.
Final thoughts
Technology cannot replace the human touch. No amount of intelligence, hundreds of thousands of lines of code, and complex algorithmic constructions can trump the human element. AI and ML could be the continuation of a beautiful friendship between humans and machines.
If you are looking for innovative ways to automate your workflows, streamline operations, and deliver personalised user experiences, partner with ELEKS. With more than 30 years of experience providing data-driven solutions for clients in the finance sector.
