The Exchanges

Every argument clarity score on this site is built from rows on this page. Each question and answer was assessed with names hidden, the host's own answers included, on four things from 1 to 5: directness (does it answer the question asked), coherence (do the ideas follow), precision (concrete details and clear references), compression (says a lot per word). The weighted mix (30/30/25/15) is the exchange score. A person's published score averages their exchange scores on raw tape only, at least 8 of them, shrunk toward the cohort mean. Full method →

Monish Anand no published score: no usable exchanges on raw tape, and a fair score needs 8+ record → ← everyone

Every exchange below was scored with names hidden, four dimensions each from 1 to 5. An exchange's score is 0.30·directness + 0.30·coherence + 0.25·precision + 0.15·compression. The published score averages the raw tape exchange scores and shrinks small samples toward the cohort mean, so five great answers can't beat twenty good ones. Produced feed rows count only toward coarse estimates, never toward a full score.

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1exchanges match
0on raw tape
0redirected or not addressed
Partly produced feed D 3 · C 5 · P 4 · Cm 4 4.00

Q And I'm, I'm excited to have you. I really am. Uh, the financial technology space is a hot space. So tell us more about data science. What does your company do and how do you generate revenue?

A Fantastic. So we, we started a company, uh, last year. I mean, obviously the company got incorporated on ninth March, 2015, but a lot of work happened before nine March, 2015. And what we're really trying to do is, uh, we are trying to give credit where it's due. In India, over four hundred million people every year apply for loans, and less than one or seven actually manage to get a loan from banks and banks in a non-banking company. So the gap is massive. So the people who don't really get loans from banking and NBFCs, their options are either to go to friends and relatives to borrow, or loan sharks are always an option here. So what we are trying to do is essentially bridge the gap between the credit, uh, uh, givers and the credit, these credit takers. And, uh, that's, you know, we are trying to build an algorithm, uh, wherein we have an innovative way of looking at credit scoring. We credit score these people. We help them understand the credit score. And once they understand the credit score, we, uh, help them get a loan from the banks and NBFCs.

AI assessment note: “We credit score these people. We help them understand the credit score.”

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