Credit has a vocabulary problem. The people building its machinery speak of models, rules, pipelines and monitoring. The people waiting for an answer speak of a car, a shop, a school fee or a plan. Chandan Pal has made a career in the space between those languages. As chief marketing officer of Scienaptic AI, he explains software that helps lenders decide whom to trust. His more interesting subject, though, is how a system earns trust in return.
Pal is based in New York, but his professional route began in India and did not resemble a neat march toward marketing. He studied computer science engineering, completed an MBA at the Indian Institute of Management Indore in 2009, and worked across design thinking, solutions, presales and user experience. At Tech Mahindra, beginning in 2007, he handled commercial-bid solution architecture. At Genpact, from 2010 to 2016, he designed process blueprints, built tailored sales material and led solutions for complicated command-center implementations.
This is useful preparation for a marketer asked to explain artificial intelligence in lending. A model may fit on a slide. A working decision system never does. It must collect data, translate it into signals, fit the lender's policies, return an answer quickly, monitor itself and leave behind an explanation that survives scrutiny. Pal came to the story after spending years around the plumbing.
The engineer enters marketing
In 2016, Pal became Scienaptic's CMO. Public descriptions of the job emphasize data visualization, storytelling, go-to-market strategy and brand positioning. The nouns are softer than architecture, but the method is familiar: identify the moving pieces, arrange them coherently and make the whole thing usable.
His writing from that transition year shows a marketer suspicious of easy stories. One essay objected to the casual abuse of “differentiation” across technology and business-services websites. Another examined the Sensex surge and argued that confidence had run ahead of economic fundamentals. These were different subjects with the same reflex: look past the declared position and inspect the mechanism beneath it. A brand is not distinct because its copy says so. A market is not sound because its index is rising. By the time Pal began telling Scienaptic's story, he had already published his impatience with the gap between a claim and its evidence.
That skepticism gives his later optimism some ballast. He is plainly enthusiastic about AI's capacity to widen credit access, but enthusiasm arrives with conditions. The system must be integrated. The decision must be monitored. The result must be explainable. A promise, in Pal's preferred grammar, is only the opening clause; execution finishes the sentence.
Scienaptic was founded in 2014 around AI-driven credit decisioning. Its pitch to lenders is that traditional files and old underwriting instruments can be too blunt. A borrower may be invisible to a conventional score while leaving meaningful evidence elsewhere: cash flows, payment behavior, transaction history or other consented data. Read those signals well, and a lender may discover qualified borrowers without relaxing its appetite for risk.
“These models allow us to approve customers that wouldn't have otherwise had access to credit.”Chandan Pal, on new lending data
Pal's language generally keeps two promises in the same sentence: more access and disciplined risk. That pairing is not accidental. Inclusion without sound underwriting is temporary; automation without explanation is brittle. In his public comments, the recurring verbs are approve, monitor, explain and serve. They belong to operations as much as promotion.
Optimism gets audited
Pal once described underwriting as the place “where trust meets math and where optimism goes to get audited.” It is a neat line because it allows the applicant and the institution into the same frame. Every application contains a future tense: the borrower will repay, the new vehicle will create mobility, the small business will produce income. The lender's job is to examine that future without confusing caution with blindness.
In 2026, writing with Scienaptic's APAC leader Joydip Gupta, Pal argued that Indian banking's AI problem was not a shortage of pilots or impressive models. The gap was production: live data pipelines, real-time delivery, reliable integration, feedback, monitoring and governance. A prediction arriving two days late is an observation. An unexplained prediction is a regulatory problem. A model separated from the loan officer's workflow is an ornament with a probability attached.
Their answer preserved a role for the credit officer. A system can digest years of cash flow, flag anomalies and prepare a risk assessment in seconds. The officer can then spend time on context, intent and edge cases. Pal's public case for AI is therefore less theatrical than the familiar replacement story. It is about giving a person with responsibility better instruments before asking for a decision.
Richer evidence does not make judgment obsolete. It makes judgment better informed, faster and easier to explain.
A balance sheet, and the life around it
The same logic animates Pal and Gupta's argument about small-business lending in India. A traditional balance sheet is a useful document, but it was designed for enterprises with fixed assets, finance teams and orderly annual disclosures. A family distributor or a small logistics operator may be financially active and poorly represented on paper. The business exists in motion while the file catches it standing still.
Digital payments, tax records and bank transactions offer a moving picture. The writers argue that the country has already built much of the infrastructure needed to see it. The remaining question is institutional: what will a lender accept as first-class evidence? Change the evidence, and the credit officer can distinguish an undocumented business from an unsound one.
This is where Pal's two educations meet. Engineering asks whether the signals can be gathered and processed. Management asks whether an organization will change the way it decides. Marketing asks whether anyone can explain the difference without exhausting the room. Financial inclusion depends on all three.
The people behind the stage
Pal's posts also reveal what catches his eye when he is away from a product diagram. After a credit-union conference, he wrote about the workers behind the stage: venue scouts, booth crews, badge printers, production staff and volunteers solving surprises before visitors noticed them. Speakers and ideas receive applause. Pal directed his thanks toward the people who made the applause possible.
That instinct had appeared years earlier. In a 2017 essay, he retold the story of a risky 1948 Dakota flight into Leh and converted it into lessons for startups. His list favored execution over novelty, leaders willing to share danger, bosses willing to share credit, and organizations able to keep their constructive oddballs. The article closes by asking companies to make room for people who question, tinker, dream and create.
One later anecdote put the principle inside Scienaptic. Pal recalled hiring a UI engineer with a gift for UX. The engineer kept objecting to the back end, challenged meetings and arrived with code. The company's response was unusual: it put engineering under him. He fixed what he had criticized and later went on to lead engineering elsewhere. Pal remembered the episode with pride and a touch of nostalgia.
“Great ideas require great execution.”Chandan Pal, writing on startup leadership
There is a quiet continuity here. A credit model has to survive integration. A conference has to survive the night before opening. A promising hire needs authority proportional to his objections. Pal's preferred stories are about the journey from an attractive claim to a working arrangement. Marketing, at its best, does not disguise that journey. It makes the labor visible.
The next chapter is infrastructure
Pal has now spent a decade at Scienaptic. His public remit has widened from explaining a young platform to speaking about the operating layer beneath lending. Recent commentary ranges from real-time monitoring of gold-loan portfolios to the place of long-term savings in household financial resilience. Yet the central question remains stable: can institutions see enough, soon enough, to make a fair and defensible choice?
He is also helping frame a community around that question. In posts about Scienaptic's ELEVATE gathering, Pal described a forum for leaders across lending, risk, compliance, data, operations, technology and member experience to question and co-create what comes next. The roster is the message. No single department owns a credit decision once software, policy and human consequence are properly connected.
The easy way to talk about AI is to point at speed. Pal's work suggests a more exacting standard. Speed matters when it carries the right evidence. Access matters when the risk remains understood. Automation matters when the outcome can be monitored and explained. The future he describes is not a loan approval stripped of people. It is a better-informed institution meeting an applicant with fewer blind spots.
That is the human case inside the technical one. A credit file is an abstraction; a decision lands in a life. Pal's job is to keep both facts in view, then find language sturdy enough to carry them together.