Pratham's Profound AI Rep
Pratham's view on credit model design shows how he thinks about proprietary data as an analytical edge.
Pratham Bajaj
Edges
Building Financial Models from Primary Research
Pratham builds financial models that are grounded in primary data collection, not just secondary sources. He designs the research instrument, runs the fieldwork, and then constructs the model around what the data actually shows. This pattern appeared across the MCA product at Indus Insights, where he ran a 5,000-respondent feasibility survey before building the credit model, and at Voyage Capital, where he built sector conviction from macro reports and annual reports before making a fund allocation call. The distinguishing feature is that he treats primary research and financial modeling as a single workflow, not sequential handoffs. The survey shapes the model; the model shapes the recommendation. For teams doing investment research or product analytics, this means he can own the full analytical chain from question design through to a defensible financial output.
Spotted in 3 Stories
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Building Credit and Risk Frameworks
Pratham builds credit and risk frameworks by identifying the proprietary data advantage first, then layering external signals to calibrate rather than decide. On the MCA product, he anchored the lending decision on the client's own gallon-spend data, a source no bank had access to, and used D and B variables only to set the margin rate and screen for obvious hazards. The framework was designed to be simple enough for a salesperson to operate. He also built the downstream segmentation model that identified three customer tiers based on repayment behavior, and the volume-tracking model that flagged stations gaming the repayment structure. The pattern is consistent: he looks for the data edge the organization already has, builds the decision logic around it, and keeps the output operationally usable.
Spotted in 1 Story
Forming Conviction from Behavioral Signals
Pratham forms investment views by anchoring on behavioral data and structural economics, not just market sentiment or headline trends. On quick commerce, he observed consumer adoption patterns directly and built a thesis on India-specific unit economics at a time when the prevailing view was that the model was unsustainable. On defense, he spotted a sector moving during an election cycle, built the analytical foundation from scratch, and identified remaining upside after the initial rally. In both cases, the conviction came from combining a behavioral or macro observation with a structured financial analysis, not from following consensus. This is an emerging pattern across two contexts, and it is the kind of analytical instinct that investment research roles are built around.
Spotted in 2 Stories
Pratham Bajaj
About Me
Pratham Bajaj is a financial analyst and consultant with experience spanning credit modeling, primary research, and equity analysis across fintech and listed markets.
He is currently wrapping up a consulting engagement at Indus Insights, where he has been the individual contributor on a merchant cash advance product for a US-based fleet fintech, owning the work from feasibility through credit model design.
At Indus Insights, he built the credit model underpinning a portfolio of over 1,000 advances at an average ticket size of $110,000, and designed the segmentation framework that drove a 16% quarter-on-quarter reduction in bad debt.
At Voyage Capital, IIM Indore's student-led investment fund, he managed a corpus of INR 16 lakh across equity markets, led sector coverage for telecom and oil and gas, and made a conviction call on quick commerce in India when market consensus was skeptical.
Pratham Bajaj
The data edge you already own is worth more than any external dataset you can buy. Build the model around what you have.”
Pratham Bajaj
On credit model design