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The impact of machine learning and analytics on the banking sector

By August 21, 2019

The financial services industry is undergoing digitalization at a dizzying rate, and this has resulted in a massive increase in the amount of internally generated data. Rapid increases in processing power and storage per dollar, combined with advances in machine learning and other analytics techniques, now offer financial institutions a high-value opportunity to leverage their growing wealth of business data to improve customer service, boost operational efficiency, and reduce risk.

Using machine learning is a major step up from traditional reporting and descriptive analytics. By enabling computers to detect patterns in transactions and market indicators, machine learning helps financial organizations identify customers at risk of churning and accounts with a high probability of exhibiting suspicious activity.

Machine learning also helps banks discover customer preferences that could guide the design of products and services. Financial institutions can also use machine learning to predict which product or service a particular customer is most likely to purchase, which improves their cross-sell and upsell efforts. This kind of targeted, data-driven approach yields higher conversion rates while reducing marketing spend.

These are just some of the ways that machine learning can create a competitive advantage for financial institutions. For a more in-depth look at the role of machine learning and advanced analytics in the banking and financial services industry, please download our white paper, “Analytics-Led Future of Banking”. We also provide examples of how Visionet has helped financial organizations achieve specific business objectives using these technologies.