Artificial Intelligence (AI) has come a long way since 2016, when AlphaGO, a computer program, first defeated an 18-time world champion at the game of GO. Artificial intelligence is profoundly increasing value across a range of industries. The banking and finance industry is no exception. There is a transformative impact to fully adopting AI in banking and finance. According to a study by Mckinsey, AI can add up to $1 Trillion of additional value to the global banking industry annually.
Admittedly, AI in banking and finance isn’t new. The industry already uses a variety of technologies to detect suspicious activity. However, the uptake is low. Most banks are still manually entering data, filling forms, reconciling transactions, and consolidating general ledgers. There is also the fact that most bank data is either fragmented, locked in silos or source systems, or is in a highly aggregated form, making it difficult to use for AI.
Below are the most compelling reasons to fully embrace AI in banking and finance.
Compliance and Fraud Detection
Compliance with industry regulations is a top priority for all banks. In the US, there are over 30 federal acts and banking regulations that affect the banking industry.
With the fear of monetary fines, criminal charges, and operational shutdowns looming, executives are always looking to reduce risk across every line of business.
AI can help by reducing errors in compliance reporting that humans typically make when manually completing them. For example, AI automates due diligence processes, which allow financial institutions to maintain accurate records regarding any customer at any given time.
In addition, AI can analyze large amounts of data and identify suspicious transactions. For example, money launderers often transact in small amounts to avoid detection. Using AI, banks comb through billions of transactions and flag any that meet specific criteria.
Also read: Best Use Cases for Virtual Data Rooms
While interest income remains the bread and butter of traditional banking institutions, many banks now rely on non-interest revenue for a significant portion of their earnings. Non-interest income includes fees, commissions, and investment income. With regard to investment income, the financial institution has to assess several options and decide which provides the best return at an acceptable risk.
The investment valuation process includes many complex calculations taking place behind closed doors throughout the course of an average workday. The process involves collaboration between multiple teams responsible for different aspects of investment asset management, credit analysts, portfolio managers, and product specialists. These teams must weigh different approaches to investing, such as allocation of funds across various asset classes, diversification among industry sectors and currencies, market timing in terms of when to enter a transaction or liquidate an investment position.
The AI solution for this is an application that can process large amounts of data from multiple sources in real-time while learning each analyst’s preferences and biases regarding investments, risk tolerance, and time horizon. In other words, an algorithm will determine which options are best based on fundamental and technical evidence instead of relying on human discretion alone.
In banking, there are many types of costs, but one primary type is labor cost. Compensation and benefits are the single biggest expense category for most financial institutions.
AI can increase the efficiency and productivity of individual workers. For example, decision management systems (DMS) allow humans to make smarter decisions faster. In addition, DMS can assist with faster client onboarding by using predetermined answers to standard questions. For example, customers fill out an online application, and the answers they provide determine the type of account available to them. With this technology, the company requires fewer front-line staff.
Human error increases reputational and regulatory risk, which often comes with dire financial consequences. DMS eliminates this risk by ensuring that data is entered into the system accurately and consistently across all channels.
In addition, AI reduces labor costs by optimizing capital investment decisions and reducing forecasting risks. For instance, banks may use predictive analytics when deciding whether or not to approve an auto loan application based on historical data such as credit scores, home values, and time since the last job loss.
Credit Valuation and KYC
Banks are required to conduct due diligence before opening an account. The documentation necessary for this process varies depending on the customer’s profile.
Credit valuation can be a labor-intensive process, especially when it comes to knowing whether or not there is enough information available regarding each client and their creditworthiness.
AI helps solve this problem by performing automated checks against internal databases and external data sources such as central banks, national statistical agencies, public registries (i.e., property registers), company registry agents, and social networks. This allows financial institutions to maintain accurate records at all times about any customer, reducing the regulatory and reputational risk associated with KYC non-compliance.
Quality service is one of the most sought-after factors when it comes to banking products. As a result, customers increasingly demand excellent customer service from their providers, and AI can help deliver on that expectation by providing personalized service with speed, precision, and convenience.
One example involves chatbots such as Facebook Messenger, Whatsapp, or Skype for Business Chatbot. These conversational interfaces automate repetitive tasks so humans can focus more on complex issues. Another great aspect about chatbots is that they learn through machine learning algorithms based on interactions with customers. This allows them to offer better suggestions over time while increasing retention rates among existing clients because people tend to purchase items they already know something about rather than risk making a bad decision because they don’t have enough information.
Disadvantages of AI in Banking and Finance
I would be remiss if I painted a rosy picture about AI without mentioning a few caveats. Three issues are worth mentioning:
- Bias: AI systems are trained by humans. So, if the training dataset is infected with human bias, the AI will inherit it. This could, for example, lead to loan applications being rejected or priced at higher interest rates without any logical justification. A human being must be able to justify any decision taken by the AI.
- Client’s remain suspicious of AI: People still prefer to deal with people. The number one thing that keeps customers returning to a bank or financial institution is excellent customer service. And, while chatbots are becoming better at providing this type of personalized attention, it will take some time for them to gain complete trust among banking clients, leading to lost opportunities in the interim.
- Cost: Software isn’t cheap, and bleeding-edge AI technology often comes at a premium. Software fees are paid upfront, and there are updates to contend with. The total cost of ownership of an AI decision management system is higher than traditional banking systems.
Check out this course on Artificial Intelligence in Banks to learn more!
The Key Takeaway: Competitive Edge
Despite these potential setbacks, artificial intelligence (AI) is revolutionizing the banking and finance industry. Banks can reduce costs and increase revenues by fully adopting AI in banking and finance, which provides a competitive edge over other financial institutions that do not embrace change.