To understand the future of Fintech (financial technologies) you have to look back at where we started. Artificial intelligence (AI) is no stranger to the banking industry. Early forms of AI technology, such as automated check processing, debuted in the 1950s, and the first automated teller machines (ATMs) arrived on the scene in the 1960s.
But a lot has changed since then. AI has evolved dramatically, especially in the last decade. Advances in software, cloud computing, machine-learning algorithms and processing speeds have given the financial services sector new opportunities to fight cybercrime, improve productivity and reduce costs.
At the same time, tech-savvy customers demand more from their digital banking experience. They’re so accustomed to interacting with Siri, Alexa and other AI-based virtual assistants, they expect similar experiences when dealing with their financial institutions. More and more banks have come to realize that AI is transforming the future of FinTech, or financial technologies. To reap the benefits, they have zeroed in on a number of key applications.
Advanced Pattern Detection Helps Fight Crime
- Fraud Detection
For years, major credit card companies have used AI in the fight against fraud, monitoring account activity patterns and flagging irregularities for further review. But new technologies have brought the practice to a whole new level.
Feedzai, a data science company backed by Citi Ventures, CitiBank’s investment and acquisitions unit, has developed advanced machine-learning technology banks can use to detect and prevent fraud in real time. The technology gets progressively smarter as it collects and processes big data, so it can quickly adapt to new types of fraud.
Similarly, FICO employs an adaptive technology in its Falcon Fraud Manager. The product uses predictive analytics that adapt to changing fraud trends and detect up to 50 percent more fraud than rule-based systems, according to the company.
- Anti-Money-Laundering Detection
As money-laundering schemes have grown more sophisticated and global in scale, traditional rule-based detection systems have become more unwieldy and expensive to operate. Why does FinTech matter? Modern AI technologies output more meaningful data to help banks identify suspicious patterns and catch instances of money laundering faster and more accurately than ever before.
QuantaVerse, for example, works solely with the financial services sector to help banks fight financial crime. The company’s platform uses a variety of data science techniques to analyze vast amounts of information from different data silos to help clients pinpoint criminal activity and reduce their risk.
ThetaRay’s big-data analytics platform operates rule free, detecting suspicious activity in its earliest stages and identifying new patterns before they become major threats. The company claims its technology offers high detection rates and a low rate of false positives to help analysts focus on investigating real issues.
Making Automated Customer Experiences More Personal
- Chat Bots
A growing number of banks are looking to chat-bot technology to transform the way they manage customer relationships.
Chat bots use AI-based systems to simulate human chats without the human intervention. During a text chat with a customer, chat bots identify the context and emotions of the end user and respond with a suitable reply. Over time, they accumulate huge amounts of data on user habits and behaviors, which helps them provide more meaningful answers.
Last year, Bank of America debuted its virtual assistant named erica. Erica “leverages predictive analytics and cognitive messaging” to provide financial guidance to the company’s more than 45 million customers. As part of its mobile banking experience, erica is accessible 24 hours a day, seven days a week to perform day-to-day transactions. She can also anticipate customers’ financial needs and provide recommendations to help them achieve their financial goals.
“We want to be there for customers in the moments that matter most,” said Thong Nguyen, president of retail banking, Bank of America. “Incorporating artificial intelligence into our mobile banking offering will help customers manage their simple banking needs more efficiently and consistently, which then allows our specialists in our financial centers to spend more time with customers to understand their more complex needs and help them improve their financial lives.”
Clinc, a FinTech startup, has created a conversational AI platform named Finie that allows banking customers to interact with their accounts by asking natural questions instead of choosing from a set of pre-determined commands.
Rather than being limited to the question, “What is my balance?,” customers can ask Finie, “Can I afford to spend $200 on concert tickets?” Instead of giving the command, “Provide a list of transactions,” clients can ask Finie, “How much did I spend on utilities?” or “Did I spend more on groceries this month than last?”
- Customer Recommendations
Log in to your Amazon account and you’ll notice the page is filled with “recommendations for you” that are based on your past browsing and purchasing history. Facebook constantly notifies users of “people you may know.” What’s behind it all? Recommendation engines powered by AI algorithms.
Well established in e-commerce applications, recommendation engines are gaining traction in the financial services sector where banking products, such as accounts, credit cards and even investment strategies, can be recommended based on user preferences and history.
Companies such as DeutscheBank, Barclays, Bank of Montreal and Huntington Bank, for example, use the Strands Finance Suite, a FinTech software that enables them to enhance their digital banking experiences with automated personal and business financial management, product recommendations and customer-linked offers.
At a recent banking forum, Strands vice president of product strategy, Oscar Sala, explained why FinTech matters: “In the future, banks will have a very different relationship with their customers, offering them the service that best fits their needs at any time. This new banking paradigm will require banks to be one step ahead of their customers and be agile enough to make personalized and contextual service recommendations, both of their own and from third parties…”
Banks Slow to Adopt Modern AI Technologies
While many of the leading players in the banking industry – JPMorgan Chase, CitiBank, Bank of America and others — have made major investments in AI technology and staffing, most banks and credit unions are still weighing their options.
In a joint study by the National Business Research Institute and Narrative Science, only 32 percent of financial services executives surveyed confirmed using AI technologies, such as predictive analytics, recommendation engines, voice recognition and others.
Firms not using AI technology cited a number of issues that are holding them back, including fear of failure, siloed data sets, regulatory compliance and unclear internal ownership of testing emerging technologies. In fact, only 6 percent of respondents employed an innovation leader or executive dedicated to testing new ideas. Among the financial institutions not using AI, 12 percent referred to AI technology as new, untested and risky.
PwC’s 2016 Global Data and Analytics Survey: Big Decisions revealed similar challenges. In fact, two-thirds of U.S. financial services respondents said they’re limited by operations, regulations, budgets or resources limitations when it comes to AI technologies.
AI also poses a challenge for FinTech firms looking to service banks. “Most big technology providers are cloud based,” Dror Oren, chief product officer and co-founder at Kasisto, explained in a Forbes story. “To be adopted by banks, you have to support on-premise deployment where banks’ internal servers run your software.”
Future of FinTech
Despite these challenges, financial services firm PwC offers several tips to banks as they explore technologies that will become essential to staying competitive in the marketplace:
- Pick two different types of problems you want AI to solve: operational issues where AI can result in productivity improvements and exploratory issues that may yield new insights.
- If you’re not in a position to set up a new chief AI officer role, make AI an extension of your current analytics team.
- Find the right balance between humans and machines – there’s a balance between optimizing servicing costs and providing good customer service.
Anand Rao, PwC innovation lead, analytics, characterized the relationship that will make AI effective for financial institutions as they move forward, “Artificial intelligence can help people make faster, better and cheaper decisions. But you have to be willing to collaborate with the machine, and not just treat it as either a servant or an overlord.”
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