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Q&A: How can AI be used in banking?

TLDR

The financial services or banking industry is an essential part of our everyday lives but the institutions who adopt and integrate artificial intelligence (AI) will have a clear advantage for their future business success.

Outline

  • Banking as you know it

  • AI use cases today

  • Case studies

  • Conclusion

Banking as you know it

Traditionally, banks provided consumers a safe and secure method of saving and storing their money, credit to buy large purchases such as homes and automobiles, and other services such as wealth management. Though the general purpose of banks and financial institutions have remained the same, the way we “bank” has changed significantly within the last few decades.

With the rise of telephone and internet banking in the ’80s and ’90s and now with the disruption of fintechs, we’ve gone from going to a brick and mortar institution or ATM to “pull out cash” to a more cashless society of peer-to-peer (p2p) payments such as Venmo, PayPal, Zelle, or Cash App. We can’t forget contactless payments such as Apple Pay, Google Pay, and Samsung Pay that may have you wondering if we even need banks at all.

These drastic changes came about with the investment in technology and the ever increasing amount of data. From banks to credit unions to fintechs, they were able to leverage data to improve customer experience; making the process of banking easier and more personalized. According to a recent Deloitte survey, 86% of financial services AI adopters say that AI will be very or critically important to their business’s success in the next couple of years. AI has already transformed the banking industry globally in a short span of time. We’ll take a look at some ways AI has impacted and shifted the financial services industry.

AI use cases today

Fraud detection

You’re enrolled in it. You could’ve had someone pose as you today and not even know, because AI is protecting you. AI is helping with the fight against money related fraud and scams through fraud detection. It follows the steps of detection, investigation, and then “dealing with it.”

The process stems from an unusual pattern of the payment transaction. Applying AI on your behavioral patterns determines whether the payment is legitimate or not, taking into account frequency of purchase, and location of prior purchases. It’s not about comparing handwriting or signatures, especially in the world of contact-less pay.

In the case that it is flagged as potentially fraud, the bank launches an “investigation” by texting the phone number of the owner’s account. Then, the owner gets a notification to verify whether the transaction is real or illegitimate.

Image descriptionOnly trust verified application notifications not text messages (Source: BofA)

Lending

At some point in our lives, we may decide to make some large purchases such as buying a car or home. It’s not uncommon to borrow from a bank to make such purchases under the right terms and circumstances. If you’ve ever applied for a loan, you know how nerve wracking it could be or maybe even frustrating. In the past, the manual verification process of lenders to check transaction history, credit scores, and other factors could take many hours, if not days, to get your approval. AI-based credit decision systems today can analyze consumer transaction data and determine eligibility for the loan in the matter of minutes.

Image descriptionCan I get an AI-men! (Source: Meme Generator)

Furthermore, AI is reducing the potential for human error and bias in underwriting and loan origination. Bad underwriting was a huge factor in the ’08 recession. AI companies like underwrite.ai apply machine learning to radically outperform traditional scorecards in both consumer and small business lending while mitigating human errors.

Risk management

It is common to have an actuary at a bank to handle risk management. In other words, determine how to calculate insurance prices and premiums. The occupation generalizes a person’s history, behavior, and other personal private information to forecast the likelihood of what will happen to them in the future.

Image descriptionIn your future, I see… AI

Similarly, AI can do risk management too. Companies can employ neutral networks to explore an infinite realm of possibilities, given the client’s personal private information.

Conversely, actuaries can use their knowledge to train regression models that make predictions, focusing on forecasting.

Customer Service

No one enjoys calling the bank to dispute a claim or a credit hold, but everyone has to do it eventually. Have you ever sat down listening to a phone tree? It ruins the customer experience, and is dreadfully slow and emotionally taxing.

Image descriptionWe’ve all been there 😔 (Source: Giphy)

Banks have multiple legal steps to get consent before processing. This is where AI can shine in banking. Using an AI chatbot to replace a phone tree helps connect customers to their goals faster by recommending relevant questions, answers, and documents.

Image descriptionNow that’s a 5/5 experience

Case studies

Does Technology Help or Hurt Morale?

The Harvard Business Review tried to tackle this question about banks switching to AI. The results were as they assumed, “no one had time to learn a complicated new system. Some people refused to attend the training. Others brought their laptops to class and worked the entire time.”

Learning a new technology is challenging and can hurt morale, especially if they’re not used to the training topics and have multiple learning spikes. It’s important to develop AI tools that are designed for end users to enjoy, with an onboarding process that builds on the basic foundations of what they currently do. The ideal onboarding process should have employees excited about learning and improving the existing customer service experience.

Image descriptionIt’s as easy as ABC, 123. (Source: Giphy)

Shift to No Signature Purchasing

Recently, banks made the decision to remove responsibility for signatures. No one reads them, or verifies them with the back of the card, and most customers scribble whatever they like. In the case study, the conclusion for this decision was because “security measures and fraud protection continue to improve making your signature unnecessary.” This is a huge benefit for AI by doing what it should be doing, removing mindless tasks that most don’t want to do, with often a low return on investment. Time spent having a human verify signatures is extra time for people to wait to get to their turn in the line.

Image descriptionAlmost there… (Source: Giphy)

Conclusion

Whether you currently work for a financial institution or simply keep your money in one, you know that technology has made the experience on both sides so much better. AI has already made a big impact (customer service, fraud prevention, risk management, automation, etc.) in banking and will have a greater presence in the industry in the near future.
Image description

Are you wanting to get started with AI but not sure where to start? Contact us to see if Mage is a good fit for you.

Co-written by Nathaniel Tjandra and Thomas Chung

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