Institutions that operate in the financial services sector commonly deal with large data sets. Whether it is millions of banking customers or innumerable stock market transactions, firms that operate in the field of finance often enjoy the benefit of large data repositories that contain deeply historical data. Machine learning algorithms are highly reliant on large data sets: good source data improves machine learning outcomes. A shortage of data, in contrast, makes any automation difficult, no matter how intelligent the underlying algorithm. Let’s take a look at the ins and outs of machine learning in finance.
Because machine learning makes use of data-dependent statistical models to draw insights and predictions, the high volume of data so commonly available in financial services is a natural fit. However, enterprises operating in the financial service sector also typically operate using legacy technology, facing real inertia to technological change.
The reason is that in highly regulated industries as finance, or healthcare, for example, the cost of a mistake is very high. Businesses are not ready to fully rely on probabilistic models to automate their decision-making processes. However, such a model can become a powerful tool for generating insights and statistics that can power more informed data-driven decisions. Therefore, to make the most of machine learning, it’s essential to understand the objectives and impact from the very start.
Financial services are built on a cornerstone of dependability, security and trust which explains the reluctance to adopt new, unpredictable technologies. That said, the emergence of new use cases of machine learning in finance, clearly illustrating the value the technology brings, is prompting many companies to reconsider. Let’s take a look.
Insurers optimise pricing. Larger losses can affect the bottom line of insurers as these big claims have an outsize effect. Insurer AXA is using machine learning to predict which car insurance policyholders present a bigger than usual risk of large losses, in turn adjusting the premiums for those applicants. Advanced data analytics allows AXA to predict these claims to a game-changing 78% level of accuracy. Besides, in this case, adopting CAT models and including simulation modelling will create an additional opportunity to mitigate risks.
When it comes to pricing, however, there is a restriction to keep in mind: avoid using reinforcement learning due to a high risk of failure.
Automated event detection. Correctly predicting the future can bring large gains to financial service providers. Foresight can tell firms when to invest and when to sell, and when to lend to a customer and when to call on a loan. Machine learning algorithms analyses vast historical data sets and apply what is learned to current events – offering calculated, valid predictions on possible market shocks and factor elasticity.
Customer data. Business intelligence (BI) used to be the buzzword in financial services, but a simplistic understanding of customer behaviour is no longer enough. Advanced data analytics including machine learning can combine customer data across channels and products to bring far deeper insights. Customer segmentation (loyal, churn risk, important etc.), customer development strategies.
Contract analysis. Contracts underpin financial services but are tedious for humans to read and interpret. The natural language processing (NLP) capabilities of machine learning enable firms to automatically analyse and interpret large quantities of contracts without the need for large staff contingents that are costly, and prone to failure. One can perform compliance comparison, to find differences or contradictions between two contract sides.
The other promising use cases of machine learning in finance include automated accounting, financial analysis and predictions; document flows optimisation. To ensure efficient trading financial institutions need to rely on a comprehensive functional analysis, and this is where commodities/currency/stocks rate prediction comes in handy.
We’ve outlined some areas in which machine learning can dramatically benefit financial services institutions and deliver large benefits for firms willing to take a strategic step towards data-driven decision-making.
Machine learning in finance relies on data and financial services firms possess data in abundance. However, enterprises operating in the financial service sector must be strategic in adopting data-driven digital innovation to make the most of these large data sets. To learn the essential steps for building and implementing an effective data analytics strategy, check out the infographic.
ELEKS has extensive experience partnering with big financial service enterprises. We have helped numerous businesses adopt fintech innovation.
Taking the first step requires a partner that understands your enterprise, your needs and pain points. Contact us to discuss how machine learning and data analytics can give your financial services firm a competitive edge.
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