The Market Scenario of AI in Banking and Finance
Given the amount of financial data and computations banks have to deal with on a daily basis and manage all, while considering industry compliances and best practices, it’s hard to disapprove that this sector is one of the most R&D Intensive with AI/ML and Deep Learning technologies.
Of course, various factors that drove the attention of banks towards AI, include fraud prevention, better risk management, personalized and improved customer service experiences, and most importantly offerings like better efficiency leading to profit growth.
Since the digital transformation in banking operations has made banks focus more on the development of skill sets, approaches, and mindsets, this emphasis applies to every functional level of the bank
If analyzing the top banks globally, Lloyds Banking Group and Banco Santander are stacking up their investment to make their organization tech-savvy by partnering with IT companies for AI/ML development services. They are hiring dedicated AI developers actively to work on their AI mission.
Accenture’s 2022 Future of Work, survey revealed that only 26% of bank CEOs have a future-ready strategy. Speaking of which, Generative AI has gained popularity in the banking sector.
As generative AI is the subject, its early adopter Singapore’s OCBC Bank has successfully tested an intelligent chatbot for its operations and efficiency requirements. After getting assurance on its compatibility with its banking operations, now it is making this Generative AI-powered chatbot for its 30,000 employees to reap its potential for their writing, translation, research, and innovation tasks. Based on their experience working with these advanced chatbots, they shared that it has resulted in a positive helping them deliver their tasks 50% faster than usual with utmost accuracy.
After testing the potential of a Generative AI chatbot for banking for their code development, document summarization, call transcription, and other knowledge base requirements, they use it to make 4M+ decisions daily in the segments of risk management, customer services, and sales. Based on their experience with this solution’s exceptional help, they are expecting to scale their dependency on decision-making for the same may increase to 10 million by 2025.
Lastly, in a 2023 survey conducted on 5,000+ C-level executives, reveals that 95% agree with Generative AI leading the enterprise intelligence landscape amongst other AI trends in Banking.
What are the Advantages of AI Banking?
It is not surprising that AI is benefiting the banking and finance sector to the advanced level with its emerging use cases for the same. If we talk about the popular AI elements and applications like machine learning, natural language processing, and computer vision, they can significantly help in improving and transforming the way banking processes are done.
Want to know why? It’s quite obvious, and Martha Bennett, a principal analyst at Forrester Research, validates that financial organizations have an upper hand on benefiting from AI. The reason for this is, “AI needs lots of data, and banks have lots of data.”
Now the question is how banks can benefit from AI and which have found their success with this?
Some of the popular advantages of AI in Banking include:
- Enhanced customer services with 24/7 customer support, personalized customer interactions.
- Real-time monitoring and detection of fraudulent transactions.
- Improved risk assessment and management through predictive analytics.
- Streamlined processes leading to cost savings and reduced errors.
- Offers advanced analytics for better decision-making and strategy development.
- Improved tracking and management of regulatory changes.
- Enhanced protection against cyber threats with AI-based security systems.
- Cost efficiency with efficient resource management.
- Reduction in human errors with AI-driven processes.
But how to achieve all these benefits? Well, that requires knowledge about possible use cases of AI in banking. And the next section is all about that.
Popular Use Cases of AI in Banking
Banking and finance landscapes are the most privileged and early adopters of AI technology for their operational benefits. From digital banking to AI-powered automated ones, the banking industry has explored various use cases or you say applications. So, some of the popular applications of AI in banking include:
1. AI-powered RPA for Improved Operational Cost and Risk Management
We all enjoy interacting with humans when it comes to seeking banking information or assistance for operations, but to make this possible, we have to make some adjustments as well. It could include timeline constraints specifically.
Additionally, banks have digitized their operations to offer convenience to both customers and employees. In banks, there’s always a need for human-based processes that are paperwork-heavy. In such cases, even the slightest error may lead banks to significant cost and risk issues.
As AI is highly known for automating tasks, personalizing services, managing a lot of processes in bulk in real-time, and evolving with new data streams, having its benefits associated with cost saving is quite significant.
An AI use case named RPA software that intelligently follows and automates digital processes based on certain rules comes into play. Integrated with natural language processing, text and handwriting recognition, and other advanced AI capabilities, RPA bots can offer a wide range of intelligent processes that were time-sensitive and error-prone before.
Some of the AI-powered RPA uses in banking operations include:
- Customer Onboarding, which includes collecting and verifying customer information, conducting background checks, and populating the necessary forms and documents.
- Account Maintenance, including updating customer information, processing account closures, and managing account status changes (e.g., freezing accounts or reactivating dormant accounts).
- Credit Assessment and Underwriting, helping in analyzing applicant data, retrieving credit scores, assessing risk factors, streamlining loan approval processes, and generating credit reports.
- AI-based fraud detection in banking operations by monitoring transactions in real time, identifying suspicious patterns or anomalies, and triggering alerts for further investigation.
Moreover, JPMorgan has been using AI-powered large language models (LLMs) for streamlining payment validation, offering faster processing, and reducing false positives. This has also helped it reduce the account validation rejection rate by 15-20%. Moreover, it also leverages AI to offer customers better financial analysis – cash flow analysis to be precise upon request.
2. Generative AI-powered Chatbots for Special Attention to Customer Service
Having robust customer service in place adds value to better customer acquisition and retaining existing customers. Not just other businesses but banks should also do that. But before digital transformation in banks, customers had to make a lot of adjustments to do their banking stuff.
A few years back, this transformation was only limited to paying bills, transferring money, requesting checkbooks, quick access to bank statements/passbooks, and more. But for advanced support, they either have to visit the bank, email, or call the customer care center. This is frustrating for many!
That’s where Generative AI jumps in! If checking banking segments and functions Generative AI contributes, then it is something like as presented in the chart given below in composition with traditional AI and analytics, Advanced AI, and Generative AI:
Generative AI in banking not only provides information asked but also acts upon certain requests in a way bank representatives do.
Bank of America uses AI for managing customer inquiries. BNY Mellon – a global investment bank that uses AI-powered chatbots to automate fund transfer requests, which helped it save around $300,000 annually and offer efficient customer services.
If we check the potential of Generative AI in banking, then annually, it creates significant new value for banks, around $200-340 billion, which is around 9-15% of the total. But the condition for the same is the optimal level implementation.
3. AI for Better Compliance to Regulatory Norms with Proactive Fraud Detection
If you check globally, you’ll find banking in the heavily regulated sector. It should be, as it stores tons of sensitive financial data of users, and its even slightest misuse can make users suffer a fortune.
Regulations ensure that banks treat their customers fairly and transparently. They establish rules around issues like fair lending practices, disclosure of fees, and protection of consumer data to safeguard customers’ interests.
They often mandate strict anti-money laundering (AML) and know-your-customer (KYC) procedures to prevent illicit activities such as money laundering, terrorist financing, and fraud.
Now, the question is, where do banks lag and seek support from AI to deal with compliances and regulatory norms?
Opting for the dedicated bank regulatory compliance monitoring service can be cost-intensive and a higher liability that some banks may not find appropriate to invest in. As a solution, many prefer to implement or integrate AI virtual assistants to actively keep watch over piling up transactions and customer behaviors, do audits, and create reports for regulatory compliance while minimizing overall risks for fraud.
As we talk about compliance and regulatory reporting for the BFSI section, it means automated compliance-based monitoring tasks, KYC checks, anti-money laundering screening, flagging suspicious activities, and much more.
A well-known bank – HSBC, has leveraged AI for anti-money laundering compliance checks from Ayasdi, which helped it reduce false positives by 20%.
Not just this, Barclays – a British Universal Bank, has also integrated an AI tool into its system, which monitors merchant payment transactions in real-time to predict potential fraudulent activities and take action against them to prevent such.
4. AI for Loan and Credit Decision Making
As we all know, banks deal with tons of data and find opportunities to onboard new customers for loans, sanctioning loans to particular ones that are too risk-free for the institution, and all while following compliances. Processing all these manually may be time-consuming, and effort-intensive and ask for additional cost investment.
Not just that, the process of loan approval also includes the practice of checking credit score, history, and finding factors that the applicant is eligible for the loan. This assuring process ensures banks that they are lending credits in the safe hands, which will pay them back with agreed interest rates.
That’s where banks can rely on AI-powered systems trained on extensive and diverse datasets that can help to analyze applicant’s eligibility for the loan sanction. It can also help banks analyze their vast customer data to find prospects who may be looking for a loan to find out business opportunities.
AI systems for credit underwriting and loan approval can help banks:
- Process data faster than humans can do, speeding up the loan approval process.
- Assess borrower’s creditworthiness with greater precision, reducing the risk of errors in decision-making.
- Evaluate various risk factors associated with a loan application, providing more reliable predictions of default probability.
- Personalized loan suggestions based on individual customer profiles considering factors such as income, credit history, and spending habits.
- Handle a large volume of loan applications simultaneously, allowing banks to scale their lending operations more efficiently.
- Continuously learn from past loan decisions and feedback, improving their accuracy and effectiveness over time.
However, when using AI tools for credit assessing, loan underwriting, and approval, you must make sure that it’s trained to be unbiased, which most such tools aren’t.
That’s where MindInventory, one of our clients, has trusted us with developing their AI banking software system that would help them predict the customer churn rate and find opportunities to retain them to continue doing better finance business.
5. AI Advisory and Automated Actions for Better Investment Management
Putting money into work is the favorite subject of everyone – be it banks or financial investors. Traditionally, banks used to put their market investment data into work, analyze those, and find better investment opportunities.
Now, with AI evolution, they are also investing in developing AI solutions for market investment that not only can offer investment opportunities but also automate them to a certain degree. It should do that based on user investment patterns, ability to take risks, and budget. Implementing such an advanced AI solution in their stock investment product could help them offer a better investment experience to their customers.
Firms like Switzerland-based UBS and Netherlands-based ING have AI systems in place, which actively analyze markets to find investment opportunities and update them to their algorithmic tradition systems to automate the investment processes. This not only frees customers from the anxieties of checking stock prices and making decisions every second but also gives them extra time to do their extensive market research to improve their portfolio and build better wealth.
Bank of America uses a platform called Glass that empowers investment bankers to find market opportunities to recommend to their customers for portfolio improvement.
Moreover, many banks also develop AI-powered robo-advisors to dedicatedly guide their customers through their investment-related queries and do better portfolio management. If these AI robo-advisors are powered with generative AI and personalization, they can offer quality guidance on investment based on their investment patterns and goals.
JP Morgan has created a tool called IndexGPT – an AI-powered robot advisor that acts as users’ personalized financial investment advisor with top-notch prediction and forecasting analysis capability.
