AI in Banking – How Artificial Intelligence is Used in Banks?
The introduction of artificial intelligence (AI) and machine learning in banking and financial sectors is gaining momentum, with banking institutions increasingly recognizing the significance of automation for crucial procedures. According to studies, about 85 percent of top-level industry decision-makers believe that AI in banking will add benefits to their enterprise processes. The introduction of AI-assisted banking apps, AI-powered document scanning services and other advanced solutions has made the sector more technologically relevant and customer-centric. Innovative AI use cases include data discovery, chatbots, loans and credits, predictive analytics, customer experience, managing and reviewing financial documents, identifying fraudulent transactions, and risk management.
AI-based technology in banking reduces costs by increasing productivity and facilitating decision-making based on machine-learning algorithms. Furthermore, intelligent algorithms can identify fraudulent information in a matter of seconds. Overall, the banking and financial sector is swiftly moving towards AI to improve efficiency, service and productivity, as well as to reduce costs.
Why the Banking Sector Should Embrace the AI-first World
Banks are in constant competition to become an AI-first sector. The banking sector has always sought to transform its operations from a people-centric business to a customer-centric one. This has led banks to take a more holistic approach to meet their changing customer demands and expectations.
With primary focus now on the customer, banks are in constant search for new and innovative strategies to serve them better. Customers now expect a bank to be available to them 24/7 whenever they need it – and they expect their bank to do it at scale. Banks can make this possible with artificial intelligence. However, to deliver on these changing customer expectations, banks must first overcome some of their own internal challenges such as legacy systems, data silos, asset quality, and limited budgets. Many banks have turned their focus towards AI as an enabler of change.
Top Applications of AI in Banking
AI and machine learning have become an integral aspect in banks and other financial institutions. Banks are integrating AI technology into their day-to-day operational workflows by including changes in the values, employment and information patterns, thereby offering better convenience to their customers. Discussed below are the major areas in banking where AI applications are making a difference:
- Cybersecurity and Fraud Detection – According to reports, the financial sector accounted for 29 percent of all cyber-attacks in 2019, making it the most targeted industry. A massive volume of digital financial transactions such as depositing and withdrawing funds, paying bills, and performing transactions via online accounts or mobile apps take place daily. Improving fraud detection and cyber security has become critical to protect customers from potential cyber threats. This is where the significance of AI in banking comes into the picture. AI technologies can help banks improve the security of online transactions, track the loopholes in their systems and minimize risk by detecting fraudulent activities and alerting clients and financial institutions. For instance, Danske Bank, Denmark’s largest bank, implemented a fraud detection algorithm to replace its old rules-based fraud detection system. This deep learning tool increased the bank’s fraud detection capability by 50 percent and reduced false positives by 60 percent. The system also automated a lot of crucial decisions while routing some cases to human analysts for further inspection.
- Loan and Credit Decisions – AI-based technologies are being used by banks to make more informed, secure and profitable loan and credit decisions. Currently, many banks still use credit history, credit scores, and customer references to determine the creditworthiness of an individual or company seeking a loan facility. Existing credit reporting systems are often erratic, they lack real-world transaction history, and show misclassified creditors. An AI-based loan and credit system can track and identify the behavior and purchasing patterns of customers with limited credit history, and thereby determine their creditworthiness and loan repayment capacity. The system will also send warnings to banks about specific behaviors that may increase the chances of default.
- Biometric Authentication – This is an important application in the banking and financial sector that determines the safety of transactions. Unique and individual-specific, the biometric verification process makes it challenging for cyber attackers to mislead clients and extract money from them. With its strong identity validation procedure, this form of authentication provides better security, offering a competitive advantage to institutions that adopt it.
- Chatbots – Chatbots are one of the best examples of the practical application of artificial intelligence in banking. By integrating chatbots into banking apps, banks can ensure that they are available for their customers around the clock. By understanding customer behavior, chatbots can offer personalized customer support and recommend suitable financial services and products.
- Tracking Market Trends – Artificial intelligence technology in banks helps process large volumes of data and predict the latest market trends, currencies, and stocks. Advanced machine learning techniques help evaluate market sentiments and suggest profitable investment options. AI can also recommend the best time to invest in stocks and warn when there is a potential market risk. Due to its high data processing capacity, this emerging technology can help speed up decision-making and make trading convenient for both banks and their clients.
- Data Collection and Analysis – Banking and financial institutions record a huge number of transactions on a day-to-day basis. Data collection, structuring and recording without any error can be a challenging task for employees. AI-based innovative solutions can help in efficient data collection and analysis, thereby improving the overall user experience. The information can also be used for detecting fraud or making credit decisions. AI also has the power to foretell future trends by interpreting data from the past. This feature, when combined with machine learning technology, can help produce data-driven predictions to counter cases of capital laundering and identify fraud.
- Refine Customer Experience – AI enhances the customer experience and makes things more convenient for them. The data gathered from customer choices and preferences enables AI to help machines create a personalized base of information for each customer. The Interactive Voice Response System (IVRS) is an example of AI-led voice assistance for customers. Voice assistance technology understands customer queries and guides customers in the right direction by routing calls to the correct department. It also assists customers with transactions and other banking-related issues in real time. AI-based software can reduce approval times for facilities such as loan disbursement. By accurately capturing the information needed to set up bank accounts, AI can ensure a smooth experience for customers.
- Risk Management – AI-powered systems enable banks to manage risk by analyzing their plans, studying failures from previous strategies, and eliminating human errors. The technology is expanding into the roots of banking security processes to encrypt each step with codes that authenticate transactions, and provide understanding to the companies on anti-fraud and anti-money-laundering activities. Regulatory checks like Know Your Customers (KYCs) help heighten security measures. AI-driven analytics can provide a clear picture of what risks are yet to come and help banks remain prepared and make timely decisions. AI also helps find risky applications by evaluating the probability of a client failing to pay back a loan. Past behavioral patterns and smartphone data are analyzed for the purpose.
- Regulatory Compliance – Most banks maintain an internal compliance team to deal with risky customer profiles and financial crimes. These processes take a lot more time and require huge investment when done manually. Compliance regulations are also subject to frequent changes, and banks need to update their processes and workflows, and follow these regulations constantly. Deep learning and natural language processing (NLP) technologies of AI help read new compliance requirements for financial institutions and improve their decision-making process. Even though AI banking cannot replace a compliance analyst, it can make their operations faster and more efficient.
- Predictive Analytics – Among the most common use cases of AI are general-purpose semantic and natural language applications and universally applied predictive analytics. AI technology can help detect specific patterns and correlations in the data, which traditional technology cannot. These patterns could indicate untapped sales opportunities, cross-sell opportunities, or even metrics around operational data with a direct revenue impact.
- Front-office Expansion – AI technology can be used to provide personalized financial guidelines to customers and strengthen security against fraudulent activities. In this way, the technology has made its way into front-office operations (customer interactions), middle-office (security) and back-end development (underwriting banking service applications).
- Process Automation – Robotic process automation (RPA) algorithms increase operational efficiency and accuracy, and reduce costs by automating time-consuming and repetitive tasks. Banking institutions successfully leverage RPA to boost transaction speed and increase efficiency. For instance, JPMorgan Chase’s CoiN technology reviews documents and derives data from them much faster than humans can.
How to Become an AI-first Bank
In this section, we discuss key strategies that banks need to adopt to implement AI on a broader scale:
- Develop an AI Strategy – The first step in AI implementation is developing an enterprise-level AI strategy by analyzing the goals and values of the banking organization. Before designing a strategy, it is crucial to conduct internal research to find gaps in workforce capabilities and processes that AI technology can fill. It is important to ensure that the AI strategy developed complies with industry standards and regulations, and current international industry standards. Next, refine the internal practices and policies related to talent, data, infrastructure, and algorithms and provide clear directions and guidance for adopting AI across the bank’s various functional units.
- Plan a Use Case-driven Process – The second step involves identifying the highest-value AI opportunities that align with the bank’s processes and strategies. It is important to evaluate the extent to which AI banking solutions need to be implemented within their current or modified operational processes in banks. After identifying the potential AI and machine learning use cases in banking, the technology team should run checks to test their feasibility. It is important to look into all aspects and identify the gaps for implementation. Depending on these evaluations, the most feasible cases must be chosen. Mapping out the AI talent is another important step. Experts such as algorithm programmers or data scientists are required to develop and implement AI solutions. If the banks lack in-house experts, they can outsource the work or collaborate with a technology provider.
- Develop and Deploy – Once the planning is done, the next step is executing the process. Before developing fully-fledged AI systems, it is important to build prototypes to understand the shortcomings of the technology. To test the prototypes, banks need to compile relevant data and feed it to the algorithm. The financial data provided must be accurate as the AI model trains and builds on this data. Once the AI model is trained and ready, banks must test it to interpret the results. Such trials will help the development team understand how the model will perform in the real world. The next step is to deploy the trained model. Once the model is deployed and data starts coming in, banks can regularly improve and update it.
- Operate and Monitor – The implementation of AI banking solutions requires continuous monitoring. Banks need to design a comprehensive review cycle for monitoring and evaluating the functioning of the AI model comprehensively. This will help banks manage cyber security threats. As a continuous flow of new data could affect the AI model at the operation stage, appropriate measures should be taken to ensure the quality of the input data.
According to reports, 65 percent of senior banking and financial management teams anticipate promising transformations from adopting AI and machine learning in the finance and banking industry. Financial institutions investing in the latest AI technologies can gain significant operational efficiency and cost-saving benefits, provide an excellent customer experience, and gain competitive advantage. Working with a business process outsourcing company can help banking institutions incorporate the latest artificial intelligence technologies into their operations.
