The banking sector is growing smarter with AI

When thinking about innovation and business domains, typically the banking sector isn’t one that readily comes to mind. But these days, tech companies are challenging banks and credit unions to improve their digital capabilities while customers are increasingly demanding more convenience and personalized service. This has led to banks spending billions on artificial intelligence which is transforming banking across internal and consumer-facing processes.

AI improves operational efficiency

One of the fundamental ways in which AI is changing industries of all kinds is through automation of repetitive, low-level tasks. Smart automation not only reduces costs, it provides employees the opportunity to spend their valuable time on more complex, creative, and customer-focused work. 

As banking institutions shift necessary but low-skill tasks to automation platforms, their employees gain the additional time needed to engage with customers in more meaningful ways. This can significantly enhance the customer experience and enable decisionmakers at banks to differentiate their companies in an increasingly competitive market.

Until recently, traditional banks have drawn criticism for not keeping pace with customers’ demands. For too long banks have been offering generic, one-size-fits-all products and features that now seem woefully out of date. The modern consumer expects personalization and dynamic digital experiences.

But to cast banks as slow-moving luddites is to miss the bigger picture. Financial institutions face complex regulatory requirements, grapple with dated cultures, and answer to a diverse set of stakeholders. Therefore, change doesn’t come easy to these established institutions and adopting new technology can take time. Plenty of time.

Today, artificial intelligence is helping banks not only catch up but innovate in many areas of operations. For example, in IT alone banks are reducing their infrastructure, development, and maintenance costs by 20-25 percent. The savings, both in terms of financial and personnel time, give these institutions the additional room needed to invest in new products and key operational areas such as security.

Intelligent process and workflow automation allows banks to reinvest personnel resources into the types of tasks for which humans are best suited. By leveraging smart chatbots for certain customer interactions, banks enable employees to again focus on more complex and interesting problems. Instead of having employees respond to the same set of common questions day in, day out, companies can now leverage AI to engage customers directly through apps or websites. Common or simple questions need never take up an employee’s time. For more complex or unique customer interactions, banking personnel can intervene and provide superior service with that newly saved time.

Data intelligence uncovered by machine learning can also lead to increased efficiency. By collecting information about customer behavior in banking apps or websites, managers gain the insights necessary to develop new product features or implement their business strategies with greater confidence.    

AI to transform risk and compliance

Banks are also finding powerful uses for AI in the area of risk and compliance. With compliance representing an enormous cost to financial institutions in the decade following the financial crisis, AI tools promise to save organizations billions of dollars collectively. As previously covered by Entefy, banks spend $270 billion on compliance with a significant portion of these expenses centered purely around personnel.

It is estimated that globally money laundering represents 2%-5% of GDP. And combating this is a costly endeavor requiring significant levels of manual effort to stay in compliance with strict regulations. AI can help banks fight the war against money laundering with the potential to reduce personnel-related anti-money laundering (AML) costs by 50%. Machine learning can be used to analyze a broad range of data points, such as location, ISP information, identity, and myriad transaction patterns. This analysis can be critical in identifying suspicious activity.

AI-powered automation would not only reduce compliance costs but could decrease risks associated with human error as well. Machine learning algorithms can be trained to detect potentially fraudulent activity by comparing current account usage against historical trends. Advanced anamoly detection can be used in sending the right alerts to specialists who can best determine whether intervention is warranted. This type of smart analysis is critical because the number of transactions that occur each day far exceed human capacity to track and flag them all.