Machine learning in European financial institutions

Study

December 2018

The entire financial services industry is being inundated with articles and presentations about the business implications of artificial intelligence (AI) and machine learning. Financial institutions are becoming aware of the potential of these technologies and are beginning to explore how advanced analytics could enable them to streamline operations, improve product offerings and enhance customer experiences.

According to Efma, “AI presents a huge number of opportunities for retail financial services firms, who, when able to exploit their growing data repositories, can better meet regulations, increase their bottom line, improve the customer experience and more.”

Financial services organizations realize they have a head start with the application of advanced analytics, since they have large data sets and experience with analytical tools. From payment services to everyday banking, insight is captured that can make machine learning more powerful.

Banks are using advanced algorithms to assist with a variety of internal and customer-facing processes. What is helpful is that consumers indicate they are willing to share personal insight if there is a value trade-off. According to a recent study, 67% of customers will grant banks access to more personal data, but 63% want more tailored advice, and the same number demand priority services, such as expedited loan approvals, or a monetary benefit, such as more competitive pricing, in return for the information they share.

Using the components of machine learning, natural language processing and cognitive computing, there are several applications within banking.
- Fraud detection.
- Meeting regulatory requirements.
- Lowering costs and increasing revenue.
- Improving the customer experience.
- Boost customer engagement.

As with any new endeavor, there are several challenges associated with the development and application of machine learning solutions. With most financial institutions in the learning phase, concerns revolve around data security, organizational impacts, the integration of new technologies and the understanding of use cases and ROI benefits.

One of the biggest challenges is finding the right talent. Another ‘people’ issue is the impact on current employees of financial institutions. In some cases, current employees will not be well positioned for the ‘new age of banking.’ In other cases, the transformation of labor caused by the advances of AI will eliminate some positions entirely.

The banking industry is still in the early stages of developing strong AI solutions. While these solutions can definitely impact the cost and revenue structures of financial organizations, the real potential is with how artificial intelligence can improve the customer experience.

Keywords : AI/Robotics , Big data