Technology that mimics the human brain is predicted to disrupt nearly every business function imaginable.
Machine learning, for example, will dramatically reduce timelines, increase quality and boost revenue. The technology, which recently improved in speed by more than 100 times, allows computers to acquire knowledge over time and apply it to complex problems. It’s become something of an answer to the question, “What should we do with all this data?” This is especially true given the growth of unstructured data coming from all corners of the financial services industry, from social posts to call center notes. By transforming data into fuel for analysis, we have created highly cognitive technology capable of, among other things, generating insights about the future.
In the financial services ecosystem, machine learning has contributed by automating the building of predictive models. These models uncover new insights that help financial institution leaders make smarter decisions. It creates the perfect blend of human and artificial intelligence.
Our data scientists have deployed machine learning techniques in myriad ways across multiple industries. In the retail sector, for instance, we designed algorithms to study different groupings of products to determine which sell best when set alongside each other on store shelves. The same could be applied to financial products to determine which bundles generate the greatest perceived and actual value for specific consumer segments.
For a large financial institution client, we recently executed machine learning strategies to improve the classification rates of credit card delinquencies and defaults. This allowed the financial institution’s cards team to understand where preventative action would have the greatest impact – both to consumers’ long-term financial health and the card portfolio’s sustainability.
Another of our financial services clients is benefiting from the application of machine learning to real-time web traffic data. Using the technology’s ability to find hidden insights (without even being programmed to do it), we have found which visitors are most likely to convert. Our next step is to enable real-time decision making based on this data using a blend of different machine learning techniques, such as reinforcement learning.
We see the potential for machine learning to help in other ways, such as cost-benefit analyses for credit card issuers. With the help of machine learning forecasts, cards teams can confidently make changes to credit lines, fully understanding the cost savings of different scenarios before determining the optimal limit for each cardholder segment.
Machine learning and other artificial intelligence techniques will also help more financial institutions leverage the power of voice recognition. This will have implications in everything from call center fraud prevention to the design of fully autonomous chatbots that can help customers make payments, apply for loans and even make smarter investment decisions.
Machine learning will not be an ideal fit for every situation. Often a more traditional model, such as linear regression, will be a better match to the desired outcomes. That’s why it’s so important to work with partners that understand the benefits and limitations of emerging technologies.
Financial distress triggers, hidden fraud and favorable past performance are other insights machine learning can deliver. Armed with this information that may have otherwise been overlooked, financial institution leaders can not only manage risk and increase revenue, they can deliver the kind of exceptional experiences today’s financial consumers expect.