Proficient software program engineer and AI strategist on accountable AI adoption in banking, enterprise innovation, and the way forward for machine studying throughout industries.
Financial institution of America introduced a $4 billion funding in synthetic intelligence and superior applied sciences, practically one-third of its whole know-how funds. The transfer alerts the rising centrality of AI in trendy finance, with functions spanning customer support, fraud prevention, and operational automation.
This huge-scale initiative locations the financial institution amongst world leaders in enterprise AI adoption, elevating pressing questions on how such applied sciences must be constructed, ruled, and scaled responsibly in extremely regulated industries.
To light up these questions, we talked to Saurav Sharma, a software program engineer with over 15 years of expertise, focusing notably on back-end structure. Saurav has delivered high-impact software program options for world companies and United States authorities companies. At Caterpillar, he resolved recurring manufacturing disruptions by growing a customized software enabling plant supervisors to appropriate database points instantly. He later consolidated twelve outdated methods right into a single world platform, decreasing operational prices and simplifying technical assist throughout services in the US, Canada, and Asia. On the transportation firm J.B. Hunt, he automated the consumer bidding course of for Walmart, producing $200,000 in further income throughout the first month of deployment.
Saurav Sharma’s technical management has prolonged to nationally important initiatives. For the healthcare reform portal often known as ObamaCare, he developed core modules for insurance coverage eligibility and hospital registration below the US Division of Well being. On the Federal Aviation Administration, he developed backend software programming interfaces (APIs) to assist worldwide flight coordination with the Swiss authorities. At Inovalon, a healthcare knowledge analytics firm, he carried out safe, role-specific person interfaces and single sign-on structure, rising platform adoption and contributing greater than $500,000 in income. His achievements earned him the 2025 Instances and Faces Award in Engineering and an invite to function a choose for the Globee Awards for Know-how, underscoring his nationwide and worldwide recognition as a number one knowledgeable in software program improvement.
Saurav, as an Onshore Lead liable for mission-critical banking software program and coordinating world improvement groups at Financial institution of America, how do you presently view the position of synthetic intelligence in banking?
Synthetic intelligence is changing into a core operational layer. We’re utilizing AI to enhance the whole lot from buyer engagement to inner effectivity and fraud detection. At Financial institution of America, for instance, AI helps our digital assistant, helps detect transaction anomalies, and automates repetitive backend workflows that beforehand consumed a major period of time.
However extra importantly, in a sector like finance, we’re making selections that may have an effect on an individual’s credit score, financial savings, or entry to monetary companies. Which means each mannequin we deploy have to be explainable, bias-checked, and compliant with evolving laws. We apply rigorous testing and validation processes earlier than any AI part goes dwell.
So, AI in banking is each transformative and delicate. It holds monumental potential, nevertheless it have to be carried out with warning, transparency, and accountability. We try to take care of that steadiness every single day.
At Financial institution of America, you helped set up an inner AI governance committee and validation processes that embody equity testing, bias detection, and documentation of mannequin conduct. How has this modified how AI is developed and deployed on the financial institution?
Implementing these constructions has considerably impacted our method to AI improvement. First, it created a shared accountability mannequin—AI selections are made in collaboration with compliance, authorized, and enterprise groups. This has helped us construct inner belief and make sure that everybody understands the dangers and targets behind every mannequin.
Second, the validation course of offers a constant framework for evaluating AI earlier than deployment. We take a look at for equity, flag potential bias, and doc how fashions behave below completely different situations. This makes it simpler to detect points early and clarify our fashions when wanted, for instance, throughout audits or buyer critiques. Though it has made our improvement course of slower in some circumstances, it’s safer and extra clear, which is crucial in banking.
Along with your position at Financial institution of America, you additionally function Associate and Vice President at Baanyan Software program Companies. What sorts of challenges or alternatives are you seeing as extra shoppers start to undertake AI options throughout industries?
At Baanyan, we collaborate with organizations throughout numerous industries, together with healthcare, retail, manufacturing, and training. In practically each case, AI now emerges early within the dialog, whether or not it’s automating workflows, enhancing buyer expertise, or informing data-driven selections.
The alternatives are important. We’ve helped shoppers optimize stock utilizing demand forecasting fashions, construct chatbots for customer support, and make the most of machine studying to establish and mitigate operational dangers earlier than they escalate. Nonetheless, adoption isn’t all the time easy.
The challenges range. For some corporations, the issue is a expertise hole; they don’t have in-house groups able to assist or scale AI initiatives. For others, it’s about knowledge readiness—you may’t do a lot with AI in case your knowledge is scattered or unstructured. After which there’s the side of change administration. Adopting AI typically means rethinking how groups work, and that may be simply as necessary because the know-how itself.
My position at Baanyan is to ship options and to assist shoppers realistically perceive what AI can do for them and the right way to construct towards that aim sustainably. That’s the place technique and empathy go hand in hand.
Mr. Sharma, at Baanyan, you’ve got led AI upskilling initiatives and delivered sensible AI options, positioning the agency as a hands-on chief in enterprise AI adoption. How are you making ready your groups for AI and ML in follow?
At Baanyan, I’ve targeted on guaranteeing our groups have the talents and instruments to work confidently with AI and machine studying. We began by launching structured coaching applications. Engineers study to work with real-world knowledge, construct fashions, and perceive how these fashions behave in numerous enterprise contexts. The aim is to maneuver from concept to software.
One of the impactful initiatives was with a retail consumer battling inconsistent retailer stock ranges. We developed a machine studying mannequin that analyzed historic gross sales, seasonality, and placement knowledge to foretell demand extra precisely. In consequence, they considerably decreased overstocking and out-of-stock conditions.
These hands-on efforts give our engineers sensible expertise and construct their confidence. I mentor groups, assessment concepts, and join them with consumer challenges. That is how we keep related and assist shoppers see clear worth in AI.
Wanting forward, what position do you see AI and machine studying enjoying in the way forward for banking, and the way do you intend to combine these applied sciences into your work at Financial institution of America and Baanyan?
Over the following decade, AI and machine studying will probably be central to banking innovation. We’ll see extra automation in software program improvement, IT assist, and customer support, notably utilizing generative AI instruments that may write code, troubleshoot points, or intelligently reply to inner queries. At Financial institution of America, I’m exploring integrating these instruments into our improvement pipeline to spice up productiveness and scale back operational friction.
At Baanyan, I’m engaged on creating an AI Middle of Excellence, a devoted unit targeted on constructing customized AI options for our shoppers. Now we have in depth expertise in finance and healthcare, and I purpose to broaden our mentorship for younger engineers coming into the AI area. I care about serving to them develop a powerful sense of duty.
In the long run, I purpose to leverage my background in engineering and consulting to create one thing new, a enterprise that makes use of AI to deal with real-world issues in sectors similar to fintech or human sources. There’s a lot potential forward, and I need to be on the fringe of it responsibly and with objective.
