“Banking is necessary, banks are not.” Thus remarked Bill Gates suggesting that while the banking system is important, its conventional structure might need to change with technology. Artificial Intelligence (AI) & Analytics have dominated all the new technologies/ inventions in the past few years. Together, they solved many of the problems that banks face in today’s digital world. They can pull out good information quickly and easily from the bank’s database and convert it into meaningful benefits for themselves and their customers. A report by Business Insider suggested that around 80% of the banks are aware of the benefits of AI in the banking sector. Another report by McKinsey highlighted the importance of banking and finance, which could grow as high as $1 trillion.
How does AI help the banking sector?
AI has many uses that can be helpful for banks in various ways including the following:
1. Understanding, segmenting and profiling of customers
2. Targeting, acquiring and retaining customers
3. Spending pattern of customers
4. Product selling and cross-selling
5. Maintaining and growing a profitable customer base
6. Regulatory compliance management
7. Risk management
8. Security and financial crime management
History of AI in banking
AI’s use in banking began with work automation and data analysis but now covers very sophisticated applications in the field of risk management, fraud prevention and tailored customer service. The development of generative AI, capable of creating and predicting based on massive amounts of data, is a huge change that promises to further transform banking operations and strategy. Using cognitive technology with Artificial Intelligence (AI) brings the advantage of digitization to banks and helps them meet the competition posed by FinTech players. About 32% of financial service providers are already using AI technologies like predictive analytics and voice recognition, among others, according to joint research conducted by the National Business Research Institute and Narrative Science2.
AI is the future of banking as it combines the power of advanced data analytics to combat fraudulent transactions and improve compliance. AI algorithm accomplishes anti-money laundering activities in a few seconds, which otherwise take hours and days. It also enables banks to manage huge volumes of data at record speed to derive valuable insights from it. Features such as AI bots, digital payment advisers and biometric fraud detection mechanisms lead to higher-quality services to a wider customer base. All this translates to increased revenue, reduced costs and a boost in profits.
Various uses of AI in banking
Detection of frauds
Millions of digital transactions occur on a daily basis for paying bills, withdrawing money, depositing cheques etc. Thus the need for the banking sector to ramp up its fraud detection mechanism has also increased. This is where AI can be of help. Eg, Danske Bank, Denmark’s largest bank implemented a fraud detection algorithm in its business which increased its fraud detection capability by 50% and reduced false positives by 60%.
Chatbots
It is one of the best practical applications of AI in banking. Unlike humans, it works 24*7 once deployed and keeps learning about a customer’s usage patterns. One of the best examples of such a Chatbot is Erica, a virtual assistant from the Bank of America. It handles credit card debt reduction and security updates and managed around 50 million client requests in the year 2019.
Loan and credit decisions
Banks are now using AI-based systems to make informed, safe and personalized customer decisions. These AI-based loan and credit systems can look into the behaviour and patterns of customers with a limited credit history to determine creditworthiness. It also sends warnings to banks about specific behaviour patterns that may increase the chances of default.
Tracking market trends and getting better customer insights
Cognitive systems that think and respond like human experts, provide suitable solutions based on available data in real-time. These systems keep a repository of expert information in its database called a knowledge database. Bankers use these cognitive systems to make strategic decisions and thus become useful in adopting market trends that are emerging. They also use AI for predictive analytics to have better insights into their customers. AI-driven predictive analytics can identify new areas of growth for their business and their customers and can better estimate which customers are a churn risk. For example, banks can analyze their customers’ habits, such as how often they log in or deposit money, and compare it to other data points to determine whether individual customers might be on the verge of cancelling their accounts.
Improving customer experience
Using AI in banking services improves the customer experience and user convenience. Using AI reduces the time taken to record KYC details and reduces the chances of errors. Additionally, AI-based software reduces approval time for facilities such as loan disbursement.
Some real-life examples of using AI in banking
- JP Morgan Chase has developed an early warning system using AI to detect malware, trojans and phishing activities. It provides a warning before the attack happens. It also sends alerts to the concerned bank’s cybersecurity team as hackers prepare to send malicious emails to employees to infect their network.
- Capital One’s Eno is one of the best personal banking AI assistants. It uses virtual card numbers to prevent credit card fraud.
- AI tools are being used in the UK to provide tailored investment advice and wealth management through platforms like Nutmeg.
- Chinese banks like ICBC (Industrial and Commercial Bank of China) are using AI-powered facial recognition for secure payments and to verify customer identities.
- Indian banks like HDFC and ICICI have deployed AI chatbots (such as EVA by HDFC) to assist customers in managing their accounts and providing quick answers.
Challenges and opportunities
While AI-based systems play a significant role in decision-making processes by reducing errors and saving time, they may also contain biases from past instances of human error. Even minor inconsistencies in AI systems can pose substantial risks to a bank’s reputation and operational integrity.
With the ever-increasing digital transactions, there will be a large data trail from the opening of accounts to day-to-day credit/debit/wallet transactions. Banks need to adapt to the latest technologies to store and manage this unstructured data in real-time. To gain a competitive edge, banks must analyze this data, extracting valuable insights which in turn add significant business value.
To conclude, Artificial Intelligence and Analytics together provide much more strength and help banks to boost organizational success and achieve their main goals viz., performance, profitability, compliance, competitiveness and risk reduction. They are the game changers & can help banks distinguish themselves and remain competitive in the future data-driven world.
