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4 ways banks can leverage the power of machine learning

The financial industry has faced waves of changes over the last two centuries. Emerging nations, the American gold rush, the power of the stock market, and even the Great Depression have all shaped how banking works and what consumers expect from their banks.

Notably, from 2015 onward, bankers began to list technology risk among their top five concerns1. While these changes have increased banking access and options for the average consumer, they also brought in more tech-savvy competition and greater regulatory scrutiny as heaps of data have become digitally accessible.

Ironically, the very technological disruption that has so upended the financial industry will also be what brings new opportunities for growth and increased wallet share. This is especially true with advanced data tools such as artificial intelligence (AI) and machine learning (ML); according to one source2, 83% of early AI adopters have already achieved substantial (30%) or moderate (53%) economic benefits.

In light of the benefits machine learning can bring, we’ve compiled four major areas where we’ve seen ML used to reduce costs, increase revenue, and mitigate risk for banks. 

1. Acquire new customers

Gone are the days where marketing was limited to just a few channels; now banks must maintain an omnichannel presence in order to reach younger consumers who may not listen to the radio or watch TV. Acquiring new customers means reaching them where they are with messaging that’s highly targeted and relevant.

Yet as margins get slimmer and budgets are squeezed, reaching these consumers with targeted messaging without breaking your budget can be a challenge if you’re not careful.

How machine learning can help

Making the most of your marketing involves making the most of your data. Machine learning can help you identify trends in consumer behavior and interests, which can help you deliver the right marketing messages in the right channels at the right time.

ML opportunities

  • Identify which existing bank customers will buy another bank product
  • Score your commercial leads based on risk, profitability, and probability to close

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2. Deepen relationship with customers

Digital transformation has affected every business in profound ways, especially in the area of reaching customers and managing the customer relationship. Today’s users want a more seamless experience, more targeted messaging, and on-demand access to information, and they’ll move to the bank that can meet their digital demands.

How machine learning can help

AI and ML allow you to combine your leadership’s decades of experience with customer engagement data. So not only will you have a gut check of what customers want, you’ll have quantifiable data to back it up. Which means your sales and customer relationship initiatives will ultimately be more effective at targeting customers ready to upsell and at cross-selling more of your products to hungry buyers.

ML opportunities

  • Identify high-value customers early and engage with them differently
  • Predict the likelihood of a customer taking their deposits elsewhere
  • Identify which disputed purchases are legitimate
  • Project a customer’s lifetime value for those with a limited history with the bank

3. Reduce Financial Risk

Consumers are becoming both more credit averse and less credit worthy, which extra pressure on banks and credit unions of all sizes. On top of that, banks face increased risk caused by data breaches, fraudulent activity, and increased costs brought on by regulatory compliance3 . These challenges make maintaining adequate cash reserves more difficult than ever before at a time of increasing market volatility.

How machine learning can help

Machine learning can give you the insights needed to reduce your overall financial risk by helping you identify fraud and financial liabilities early – so you make and keep more of your profits.

ML opportunities

  • Clarify the liabilities on your balance sheet and determine which are the greatest risks
  • Detect fraud and misuse of the company’s finances
  • Project cash reserves to reduce excess bank cash


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4. Optimize investment offerings

The investment management arm of today’s banks continues to change rapidly as industry challenges increase. Today’s investment managers deal with increased market volatility, capped organic growth, and increasing fees. Because of these challenges, they struggle to keep up with shifting expectations of clients who demand a better investment turnover.

How machine learning can help

Machine learning can be used to detect patterns hidden in a bank’s historical investment data combined with external financial data. These patterns produce actionable insights that can increase the accuracy of key investment decisions.

ML opportunities

  • Match securities to investors based on trade history and market conditions
  • Dynamically price securities based on competitive offerings, market saturation, and risk profile

See how one institution used ML to predict their deposit customers' likely deposits on a daily basis, freeing $40,000,000 in excess cash reserves

The up-and-coming (and existing) opportunities for financial institutions to win with ML are staggering. One source estimates that advanced data initiatives like AI and ML are predicted to boost overall business profitability by 38% and generate $14 trillion of additional revenue by 20354.

And while it’s true that digitally savvy industry newcomers may take advantage of these trends faster than their legacy peers, legacy banks and credit unions hold something the younger competition doesn’t: mountains of historic data that, when mined for insights using AI and ML, can give them a leg up in retaining customers, increasing their wallet share, and reducing their overall financial risk.

To adequately leverage these four opportunities, banks will need to embrace the very technology disrupting the industry. Banks that view their data as one of their most important assets and embrace AI and ML to create new insights will likely see growth, whereas those who don’t will struggle to keep up.

1) 2015 Banking Banana Skins Report

2) 2017 Deloitte

3) 2017 Financial News

4) n.d., Accenture

About the author

John Dages

John Dages is Fusion’s Machine Learning Solution Director. With over 15 years of technology leadership experience, John brings a unique and essential perspective to enterprise customers’ cloud and advanced analytics journeys. By leveraging his data, application development, systems integration, and I&O background, John offers a complete picture of how companies can utilize machine learning to drive a competitive advantage and engineer true intellectual property.

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