Artificial intelligence (AI) has come a long way since 2016, when AlphaGO, a computer program, first beat an 18-time world champion at the game of GO. Artificial intelligence dramatically increases value across a wide range of industries. The banking and financial sector is no exception. The full adoption of AI in banking and finance is having a transformative impact. According to a Mckinsey study, AI can add up to $ 1 trillion in additional value to the global banking industry each year.
Certainly, AI in banking and finance is not new. Industry is already using various technologies to detect suspicious activity. However, absorption is low. Most banks still manually enter data, fill out forms, reconcile transactions, and consolidate ledgers. There’s also the fact that most banking data is either fragmented, locked in silos or source systems, or in a highly aggregated form, making it difficult for AI to use.
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 United States, there are over 30 federal laws and banking regulations that affect the banking industry.
With fear of fines, criminal charges and looming operational shutdowns, executives are always looking to reduce risk across all lines of business.
AI can help by reducing the errors in compliance reports that humans typically make when filling them out manually. For example, AI automates due diligence processes, which allow financial institutions to keep accurate records about any customer at any given time.
Additionally, AI can analyze large amounts of data and identify suspicious transactions. For example, money launderers often carry out small transactions to avoid detection. Using AI, banks review billions of transactions and flag those that meet specific criteria.
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While interest income remains the bread and butter of traditional banking institutions, many banks now depend on non-interest income for a significant portion of their income. Non-interest income includes fees, commissions and investment income. When it comes to investment income, the financial institution must evaluate several options and decide which one offers the best return at an acceptable risk.
The investment appraisal process involves many complex calculations that take place behind closed doors over the course of an average working day. The process involves collaboration between multiple teams responsible for different aspects of investment asset management, credit analysts, portfolio managers and product specialists. These teams need to weigh different investment approaches, such as the allocation of funds among various asset classes, diversification between industry sectors and currencies, market timing in terms of when to close a deal or liquidate a position in the market. ‘investment.
The AI ââsolution 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 solely on human discretion.
In the banking industry, there are many types of costs, but a main type is the cost of labor. Compensation and benefits are the most important 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. Additionally, DMS can help speed customer onboarding by using predetermined answers to standard questions. For example, customers complete an online application and the responses they provide determine the type of account available to them. With this technology, the business needs fewer front-line staff.
Human error increases reputational and regulatory risk, which often has dire financial consequences. DMS eliminates this risk by ensuring that data is entered into the system accurately and consistently across all channels.
Additionally, AI lowers labor costs by optimizing capital investment decisions and reducing forecasting risks. For example, banks can use predictive analytics to decide whether or not to approve an auto loan application based on historical data such as credit scores, home values, and time since last job loss.
Credit Assessment and KYC
Banks are required to do due diligence before opening an account. The documentation required for this process varies depending on the client profile.
Credit assessment can be a tedious process, especially when it comes to whether or not there is enough information available about each customer 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 registers (i.e. registers of property), agents of the register of companies and social networks. This allows financial institutions to keep accurate records at all times on any customer, reducing the regulatory and reputational risk associated with KYC non-compliance.
The quality of service is one of the most sought after factors in banking products. As a result, customers increasingly demand excellent customer service from their vendors, and AI can help meet that expectation by delivering personalized service with speed, accuracy, and convenience.
One example involves chatbots such as Facebook Messenger, Whatsapp, or Skype for Business Chatbot. These conversational interfaces automate repetitive tasks so that humans can focus more on complex problems. Another interesting aspect of 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 customers as people tend to buy items that they already know something about rather than risk making a bad decision because that they don’t have enough information.
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Disadvantages of AI in banking and finance
I would be remiss if I painted a rosy picture of AI without mentioning a few caveats. Three problems are worth mentioning:
- Bias: AI systems are driven by humans. So, if the training dataset is infected through human bias, the AI ââwill inherit it. This could, for example, lead to rejection of loan applications or to higher interest rates without any logical justification. A human being must be able to justify any decision made by AI.
- Customers remain wary of AI: People always prefer to deal with people. The main thing that makes customers return to a bank or financial institution is great customer service. And, as chatbots get better at providing this kind of personalized attention, it will take some time for them to gain full trust among bank customers, resulting in lost opportunities in the meantime.
- Cost: Software doesn’t come cheap, and cutting-edge AI technology often pays off. Software fees are prepaid and there are updates to manage. The total cost of ownership of an AI decision management system is higher than traditional banking systems.
The bottom line: competitive advantage
Despite these potential setbacks, artificial intelligence (AI) is revolutionizing the banking and financial industry. Banks can cut costs and increase revenues by fully embracing AI in banking and finance, giving them a competitive advantage over other financial institutions that don’t embrace change.
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