Digital banking implementation expert Prashant Bansal has over 15 years of extensive expertise in financial services, specializing in integrating and designing banking products including Money Market, Mutual Funds, and Loans. He utilizes a robust skill set in database, Java, RESTful and Microservices to deliver efficient and innovative digital banking solutions, with a dedication to enhancing customer experiences through advanced technical strategies and driving financial technology forward.
Prashant shares insights on how AI and machine learning are reshaping digital banking, discussing prompt engineering, microservices architecture, and the technical challenges banks face when modernizing their systems while maintaining regulatory compliance and security.
How are financial institutions currently integrating AI and machine learning into their digital banking platforms, and what transformations are you seeing?
Banks and other financial institutions are already actively seeking to integrate their digital platforms with the latest machine learning algorithms, neural networks, and generative AI tools. According to a Citigroup report, AI could drive global banking industry profits to $2 trillion by 2028, a 9% increase over the next five years.
A bank powered by AI offers a benefits spectrum ranging from better customer experience to increased cybersecurity. The adoption of AI at times faces regulatory challenges, especially in the finance sector, slowing down the vision to achieve AI and ML as the forefront of the technology stack. That said, banks have already started utilizing Gen AI tools to serve customers as part of their customer service AI initiative with very little to NO wait time and exact answers. Fraud detection with the help of Gen AI tools is a big game-changer for ever-evolving cybersecurity threats and sophisticatedly framed phishing attacks.
What role does prompt engineering play in developing effective AI solutions for banking applications, particularly in customer service and financial advice?
As AI reshapes the banking and finance industry, prompt engineering emerges as a pivotal skill for professionals engaged in compliance, fraud detection, customer service, and financial analysis. Cultivating this expertise is key to achieving greater efficiency, accurate results, and domain-specific answers.
Utilizing multiple prompt techniques like zero-shot, one-shot, few-shot prompts, and chain of thought prompting can make a significant difference. To take an example of a shot prompt, we can let an LLM model know the relation of credit score, debt-to-income ratio, and associated risk level. Provide a few examples of this combination and LLM would be able to find out the risk score for you, let us say while applying for a credit card application. This overall mechanism helps banks in faster and accurate results.
In your experience implementing Digital Banking solutions, what are the biggest technical challenges banks face when modernizing their systems with AI capabilities?
There are multiple challenges, but if I have to select a few biggest ones, they would be “weak and outdated technologies along with poor infrastructure management,” “fragmented or distributed data assets,” “security risks,” and “regulatory compliance concerns.”
Banks in general are tech savvy but are also reluctant to the biggest changes, as the changes are always tied to infrastructure and training challenges. Another problem is poor data management, which makes it difficult to train the LLM models to retrieve accurate responses; this could lead to hallucinations. Banks, along with their customers have to have a mindshift in order to achieve what AI has to offer. This can only come from robust security mechanisms and awareness while implementing AI services, especially when it comes to financial and money-involving transactions.
How do microservices and RESTful architectures enable more intelligent and scalable digital banking solutions?
There are solutions in the market that still run on monolith architecture and, in turn, have a very heavy-weight application to offer. This requires more infrastructure and processing power. This problem can be solved by implementing microservices and RESTful architecture by breaking down the application into multiple segments and running them as independent services communicating via APIs.
Microservices allow for independent scaling of services, ensuring seamless performance during peak usage periods and preventing the entire system from being overloaded. Microservices also provide a robust running mechanism for business continuity. Let’s say, if one microservice fails, it’s less likely that it would bring down the entire application, and other functionalities are not impacted. Hence, the distribution deployment of services helps in scaling, business continuity, and enhanced security.
What are the key considerations banks should keep in mind when balancing AI-driven automation with regulatory compliance and security requirements?
Banks should focus on data privacy, algorithm biases, hallucinations, and compliance with ever-changing banking regulations. Financial institutions handle a vast amount of customer data, which is both critical and personal in nature. There are certain country guidelines as well, along with PII data set, and hence it is the bank’s responsibility to protect and safeguard that data from going into the public domain.
When we talk about bias in AI, it’s often not something obvious. It’s built into every step, from the data we feed it to how it’s trained and even how it continues to learn. All of these stages can inadvertently lead to discrimination. Think of it this way: AI is only as good as the information and instructions it gets. If there’s bias in what goes in, you’ll inevitably get biased output. This is a big concern for financial institutions, and a key way to tackle it is by putting strong compliance solutions in place.
How can financial institutions ensure that AI implementations maintain the human touch that customers expect in banking relationships?
AI in finance can retain the human touch. The key is to view AI as an enhancer of human interaction, not a replacement.
Firstly, design AI to handle routine tasks efficiently, freeing up human advisors to focus on complex issues and personalized advice. This means AI for things like basic inquiries and transaction processing, while human bankers handle financial planning and problem-solving.
Secondly, build in clear “escalation paths” to human interaction. Customers should always have an easy and obvious way to speak with a person if the AI can’t meet their needs or if they simply prefer human interaction.
Finally, leverage AI to provide insights that inform human advisors. AI can analyze data to help bankers understand customer needs better, allowing them to offer more relevant and empathetic service.
Looking ahead, how do you see AI and machine learning reshaping the future of digital banking over the next 3-5 years?
In the next 3-5 years, AI and machine learning will fundamentally reshape digital banking, primarily by enhancing personalization, fraud detection, and operational efficiency. We’ll see personalized or customized customer experiences, with AI analyzing behavior to offer tailored products and advice.
Real-time, sophisticated fraud detection will become even more robust, significantly reducing financial crime. On the operational side, AI will streamline back-office processes, automate compliance, and optimize resource allocation, leading to faster, more cost-effective services.
The key will be leveraging these advancements while maintaining transparency and addressing inherent biases within the data and algorithms to ensure equitable and inclusive financial services for all.
What advice would you give to banking executives who are hesitant about adopting AI technologies due to security or regulatory concerns?
It’s a genuine concern and can be overcome with time. The bigger part of the problem is “trust.” If an AI’s decision-making process can be understood and audited, it significantly mitigates regulatory risks and enhances security by allowing for the identification and rectification of vulnerabilities. For banks, data training and getting rid of hallucinations should be the first and foremost step, and that will help executives to gain trust with AI.
Banks that can demonstrably prove their AI systems are secure and compliant will gain a significant advantage in a market increasingly sensitive to data privacy and ethical AI use. This involves engaging with regulators early, participating in industry dialogues, and even contributing to the development of best practices.
For organizations looking to implement AI in their banking operations, what should they prioritize to ensure successful adoption?
For successful AI adoption in banking, banks must prioritize data quality to mitigate biases, establish a robust ethical AI framework with clear guidelines to treat hallucinations, and invest in human capital to ensure employees are skilled in collaborating with AI.
Additionally, aligning AI initiatives with clear business objectives and maintaining strong regulatory compliance from the outset are crucial for both trust and tangible returns. One of the biggest opportunities lies in leveraging AI to enhance risk assessment and fraud detection. By combining banking data with advanced machine learning models, we can move from rule-based systems to real-time, adaptive solutions.
Another key area is personalization. AI can help banks tailor products and services to individual customer needs at scale, improving both customer experience and retention. Targeted product service and cross-selling based on users’ activities can help banks in servicing the customers’ tailored needs.
