All industries stand to improve revenue by utilizing artificial intelligence (AI) and machine learning (ML). The finance industry, in particular, has everything to gain by investing in AI. Analysts estimate that AI will save the banking industry more than $1 trillion by 2030.1
FinServ companies need to understand how they can use AI and ML to grow their bottom line and drive success. Here are a few ways to do just that:
Today’s consumers want more personalized communications across the board and that includes financial institutions. Recent research by Hubspot, found that 90% of customers rate an “immediate” response as important or very important when they have a customer service question.2 Financial institutions can provide personalized, instant customer service using AI-powered chatbots or Natural Language Processing (NLP) to keep customer satisfaction high.
Brand example: Axis Bank
Axis bank wanted to create an AI-powered chatbot that could enable quality, scalable, and cost-efficient customer service. The bank experienced rapid growth, and their customer service inquiries had grown by double digits year-over-year. They needed a scalable alternative to hiring additional customer service reps. The bank developed a chatbot, called Axis Aha!, to help field the growing number of inquiries.
With the chatbot, Axis was able to provide 24/7, instantaneous customer service. The chatbot also offered relevant prompts to customers, asking them if they want to pay their rent or bills at the beginning of the month.
The results: Axis reports that the chatbot has helped them reduce turnaround time on opening savings and checking accounts by 90%, and other processes, such as credit card applications, have a reduced turnaround time of 50-80%. The bank is continuing to explore AI and has identified 90 other key processes they want to re-architect.3
FinServe companies are turning to AI to evaluate credit risks more accurately.
Machine learning can use an enormous amount of data to achieve a more comprehensive analysis of a potential lendee’s creditworthiness, whether for a consumer loan or corporate credit. Currently, banks reject small and medium-sized businesses at a rate of 74%.4 As SMB’s look for alternative ways to find the cash flow they need, they have turned to FinServ disruptors such as Paypal and Amazon. Over the last few years, Amazon Web Services (AWS) has been building ML and Artificial Intelligence (AI) services to help process documents at what they describe as, “warp speed.” AWS has reported that customers such as Kabbage, BlueVine, Baker Tilly, and Biz2Credit, have turned to AWS during the pandemic to help speed up the PPP loan processing they need to keep their small businesses running.5 Amazon has made more than three billion dollars in the lending space to date.6
Financial institutions can build or work with solution providers to build their own AI/ML algorithms to better score potential lendees and shorten processing times. Especially in the post-COVID era, shorter processing times make a life-or-death difference for SMBs, so companies that can provide loans quickly have the opportunity to gain life-long customers.
Brand example: UK High Street Bank
A UK High Street Bank wanted to see if machine learning was more effective than their current credit scoring practices and if it could better predict if a customer was likely to default on a loan or credit card. They used an ML platform to ingest their data and build models in order to run tests using the bank’s CRM data.
The results: The ML caught 83% of bad debt that was not detected by an applicant’s credit score, meaning the bank’s credit risk was dramatically reduced. If the bank was to maintain the same default rate, the ML found that they could lend to 77% more people.7
Business Insider predicts that by 2023, 78% of banking will be digital. With numbers that high, financial institutions will need to invest in security, and AI is an excellent place to start. AI can analyze and single out irregularities in consumer transactions that would otherwise go unnoticed by humans.
Brand example: JPMorgan Chase
Consumer banking represents over 50% of JPMorgan Chase’s net revenue. It is therefore imperative for the bank to keep consumer data secure. To do that, they have adopted fraud-detecting AI applications for their account holders. They developed and implemented a proprietary algorithm to detect fraud patterns. Each time a credit card transaction is processed, details of the transaction are sent to central computers in Chase’s data centers. From there, the AI decides whether or not the transaction is fraudulent. Its AI-enabled security measures earned the bank second place in Insider Intelligence’s 2020 US Banking Digital Trust survey.8
AI and ML have many useful applications for the financial services industry. Improving customer experience, enhancing security, and making smarter credit decisions are a few ways the finance industry can utilize these disruptive technologies to increase growth and retain customers. The basis of all AI is clean, well-managed data supported by proper data governance. Only then can data be leveraged for these advanced applications. Stay tuned for more ways big data is changing the face of finance.
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As Content Marketing Manager, Natasia is responsible for helping strategize, produce and execute Data Axle's content. With a passion for writing and an enthusiasm for data management and technology, Natasia creates content that is designed to deliver nuggets of wisdom to help brands and individuals elevate their data governance policies. A native New Yorker, when Natasia is not at work she can be found enjoying New York’s food scene, at one of NYC’s many museums, or at one of the city’s many parks with her two teacup yorkies.