Technology Innovations Changing the Financial Institution Ecosystem

The Financial Applications for AI and Machine Learning

If you’ve been following our FIRST and SECOND installments of this three-part series on machine learning and artificial intelligence, then you are ready for our conclusion of how these advanced technologies and applications are changing the financial services ecosystem.

Artificial intelligence (AI) and machine learning (ML) have been major contributors to the banking industry well before the advent of mobile banking apps and chatbots. Both AI and ML are beginning to play an integral role in how banks and credit unions operate and interact with consumers: from approving loans to managing assets and protecting against fraud.

Though often perceived as the same concept and used interchangeably, they are quite different. In a nutshell, artificial intelligence is the simulation of human intelligence processes by machines. Machine learning, on the other hand, is basically a sub-field in the larger AI research landscape. It is the process of using algorithms to learn from data and then make predictions about similar data in the future.

Leveraging AI and ML Advantages

With the volume, variety, and velocity of data increasing rapidly, financial institutions are starting to rely on AI and ML to make the most out of the wealth of data they own for more accurate, data-backed business decisions.  Through machine learning, AI can easily and effectively consume and process large amounts of data at an expedited level. Its real-time speed offers efficiency as it continues to learn and become even more efficient over time.

Consider these key applications of AI and ML, and the benefits they offer:

Lower Costs:
AI and ML give credit unions and banks the power to bring together insights from diverse data sets to personalize marketing messages and to offer the right product to the right customer through the right channel at the right time.   ML helps with better marketing efficiency, better use of resources, automates processes and reduces wasteful onboarding promotions with better-targeted marketing campaigns—saving time and money.

Improved Customer Service and Online Experiences:

According to the Content Marketing Institute (CMI), AI can help personalize messages, offers and interactions online. Chatbots use machine learning to understand customer behavior, track spending patterns and tailor recommendations on how to manage finances. Satisfaction is greatly improved when a financial institution can provide product and service recommendations specifically tailored to individual consumers. (1)

Fraud Detection:  AI and ML programs can identify unexpected and suspicious actions for near real-time fraud detection. The value of this in cost alone is significant considering that consumers lost more than $16B to fraud and identity theft last year.  (2)  ML systems have the potential to improve detection of money laundering activity significantly due to their ability to identify complex patterns in the data and transactions.

Better Compliance:  According to Digitally Cognizant, as financial institutions are trying to rein in the cost of regulatory compliance they can unleash AI’s abilities to accelerate throughput up to three times. This could save an estimated 30% of compliance costs annually. Plus, AI is helping regulatory compliance teams interpret regulatory meaning, comprehend what work needs to be done, meet requirements, and codify compliance rules.(3)

Enhanced Revenue and Profitability:  As AI and ML enable financial institutions to better target customers with relevant marketing messages about new products and services, FIs can leverage this ability for more opportunities to onboard new customers/members and cross/upsell existing ones. These refined approaches lead to more profitable business outcomes and growth.

Improved Retention:  AI and machine learning can help identify segments of the customer or member base that are at risk of leaving for a competitor. Through machine learning and predictive algorithms, a financial institution can forecast at what point in a relationship consumers are most likely to leave and what triggers a switch so they can put controls in place to reduce attrition at those trigger points.

Increased Productivity:  The automation of many, once manual, processes that were impacted by human errors is a huge win for financial institutions. By providing continuous automation, ML offers greater speed and efficiency in many areas of operations.  Over the next few years, AI will be used to transform the most central functions in banking, such as inter-bank reconciliations and the quarterly “close” and reporting of earnings, as well as engage in the more strategic functions such as financial analysis, asset allocation and forecasting. (4)

Potential Challenges of ML and AI

Though AI and ML offer countless potential benefits to financial institutions, like with most things, there are potential pitfalls. Some challenges the industry may experience with these innovative technologies include:

  • The uncensored power of smart computers and algorithms presents financial institutions with a source of regulatory, compliance and privacy challenges.
  • Some industry experts suggest that governance of machine learning algorithms is not as strong as it needs to be. According to Federal Reserve, rules such as SR11-7 Guidance on Model Risk Managementdescribe how models should be validated, these rules do not cover machine learning algorithms. (5)
  • The complex IT landscapes burdened by legacy systems pose several challenges when adopting new technologies such as AI and ML.
  • Automation produced by AI and ML will cause an estimated 8% decline in the number of tellers between now and 2024.
  • For ML to succeed, it needs access to large datasets. This means financial institutions must make data freely available to ML software and solutions so they can ingest inputs and churn out accurate insights and predictions.

What the Future Holds

Machine learning and AI will continue to be in the limelight for many forward-thinking financial institution executives—and for a good reason. By advancing forward with AI and ML platforms, credit unions and banks can substantially reduce operational costs and significantly improve the bottom-line.

Whether your institution has already invested in new AI and ML technologies or needing guidance on how to best leverage these applications, The Knowlton Group can help (LINK THE “THE” AS WELL).  Working with banks and credit unions each day across the country, we understand how to help our clients maximize technologies to yield the greatest outcome.   Contact us today to start the discussion.


  1. 8 Ways Intelligent Marketers Use AI; Content Marketing Institute.  August 2017
  2. Fraud Cost $16B to Consumers:  CNBC, February 2017
  3. How Banks Can Use AI to Reduce Regulatory Compliance Burdens: Digitally Cognizant, June 2017
  4. AI Is Becoming a Major Disruptive Force in Many Banking Finance Departments: Forbes, February 2017
  5. Federal Reserve’s Supervisory Guidance on Model Risk Management: Federal Reserve, April 2011

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