Crisil vs Trusting Social (2026)
Two Asian analytics groups with genuine regulatory standing, pointed at opposite subjects. Crisil characterises companies, running the corporate credit lifecycle from financial spreading through rating, early warning and covenant tracking for global institutions, with model validation as a business line and its ratings arm registered with the Indian securities regulator, majority owned by a global ratings group. Trusting Social characterises people, scoring consumers with little or no formal credit history across Vietnam, Indonesia, India and the Philippines from alternative social, web and mobile data, more than a billion scored, with six of the ten largest Philippine banks named as clients and a national central bank having admitted it to a supervised programme. The measurement split is the page's finding. Crisil publishes measured performance for its own automation and submits its generative capability to external assessment across the model risk lifecycle, which almost nothing in this index does. Trusting Social publishes no accuracy, lift or validation figure anywhere, across thirteen years of operation, for a product whose entire output is a prediction about a person. The verifiability routes then swap: Trusting Social's parent files public accounts, so a buyer can read its revenue and losses directly, while Crisil's quantified outcomes attach to no named institution. Standing is real at both; the numbers behind the product exist at one.
- Your borrowers are companies and your examiners want the discipline. The corporate credit lifecycle runs from spreading through rating, monitoring and covenant tracking, with model governance, stress testing and validation sold as products alongside.
- Your automation should arrive measured. Published figures state 95 percent extraction accuracy on financial spreading and generative coverage of 60 to 70 percent of credit report sections, externally assessed across the model risk lifecycle.
- Your supplier should sit near supervision. The ratings arm is registered with the Indian securities regulator, and the group is majority owned by a global ratings and analytics company.
- Your applicants have no file. Proprietary models score consumers from alternative social, web and mobile signals, applied across more than a billion people in four markets where conventional records are thin.
- Your market proof should be named. Six of the ten largest banks in the Philippines run the scoring, more than 40 institutions use it in Vietnam, and a national central bank admitted the company to a supervised programme.
- Your diligence wants financial durability on paper. The parent files public accounts, so revenue, losses and cost lines are readable rather than asserted, a verifiability route almost nothing private in this lane offers.
This comparison is published by AI FinTech Index, an independent research platform that publishes independent ratings of AI vendors for financial services. Crisil and Trusting Social are each graded against the same capability taxonomy, from each vendor's own public materials and the regulatory record, under the AI FinTech Index verification standard. No vendor pays for placement, and no vendor has reviewed this page. How this evidence is graded
Plain facts
| Crisil | Trusting Social | |
|---|---|---|
| Primary category | Credit Decisioning & Underwriting | Credit Decisioning & Underwriting |
| Founded | 1987 | 2013 |
| Headquarters | Mumbai, India | Singapore |
| Website | www.crisil.com | trustingsocial.com |
Side by Side
| Axis | C Crisil |
T Trusting Social |
|---|---|---|
| AI Centrality | ||
| Autonomy and Oversight Model | ||
| Model Risk Management and Transparency | ||
| Operational and Outcome Evidence | ||
| AI Safety and Data Stewardship | ||
| GLBA and Data Privacy Posture | ||
| Security Certifications and Trust Center | ||
| Regulatory Status and Licensure | ||
| AI Governance and Bias Disclosure | ||
| AI Liability and Recourse | ||
| Model Supply Chain Disclosure | ||
| Core Systems and Integration Depth | ||
| Deployment Model and Data Residency | ||
| Commercial Transparency | ||
| Institution and Segment Coverage |
The short version of each
Crisil
Crisil characterises companies, running the corporate credit lifecycle from financial spreading through rating, early warning and covenant tracking for global institutions, with model validation as a business line, a ratings arm registered with the Indian securities regulator, and majority ownership by a global ratings group. The AI FinTech Index records its measurement posture as the page's finding: measured performance published for its own automation and generative capability submitted to external assessment across the model risk lifecycle, which almost nothing in the index does. The index records the gaps in the same frame: the 95 percent extraction figure quantifies roughly one field in twenty wrong with no described handling, quantified outcomes attach to no named institution, no fairness position is published, and the wrongly flagged borrower on an early warning list has no described notification or contest route.
Source: AI FinTech Index, 2026
Trusting Social
Trusting Social characterises people, scoring consumers with little or no formal credit history across Vietnam, Indonesia, India and the Philippines from alternative social, web and mobile data, more than a billion scored, with six of the ten largest Philippine banks named as clients, a national central bank having admitted it to a supervised programme, and a parent that files public accounts a buyer can read directly. The AI FinTech Index records the missing number as the product itself: no accuracy, lift or validation figure has been published anywhere across thirteen years, for an output that is a prediction about a person. The index records the input set as the most contested in its roster, social, web and mobile signals importing whatever inequality is already encoded in them, with an address inference product extending the modelling to where a person lives, no published notification, access or challenge route for the scored consumer, and an institutional customer count that has declined from a previously reported 170.
Source: AI FinTech Index, 2026
Common questions
Do Crisil and Trusting Social compete?
Barely. Crisil serves global institutions assessing corporate borrowers, while Trusting Social scores consumers in Vietnam, Indonesia, India and the Philippines, so the pairing compares two supervised adjacent Asian analytics groups with opposite subjects rather than a shortlist. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified August 23, 2026. No vendor pays for placement.
Which vendor has real regulatory standing?
Both, differently. Crisil's ratings arm is registered with the Indian securities regulator, and Trusting Social was admitted to a national central bank's supervised programme and a global card network's partner programme, which the AI FinTech Index records as regulator engagement rather than compliance claims.
Does Trusting Social publish model performance?
No. Thirteen years and a billion consumers scored have produced no published accuracy, lift or default figure, and the founding team's econometrics credentials are stated where measurement would be, a gap a lender's model risk function should press on. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified August 23, 2026. No vendor pays for placement.
What data does Trusting Social actually read?
Social, web and mobile behaviour, plus a product inferring home and work addresses from indirect evidence. That reaches people who have no file, and it imports the inequalities encoded in those signals, with no fairness testing published. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified August 23, 2026. No vendor pays for placement.
Related comparisons
Other published head to head assessments involving these vendors or their closest peers. The full set for this category is on the Credit Decisioning & Underwriting page.
The measurement inversion runs through this pair in the uncomfortable direction. Crisil publishes measured performance for automation that characterises corporate borrowers to decision makers, and its 95 percent extraction accuracy also quantifies that roughly one field in twenty is wrong with nothing describing what happens to those.
Trusting Social publishes no accuracy, lift or validation figure anywhere across thirteen years and a billion consumers scored, which for a product that is a prediction is the missing number, and its input set is the most contested in the roster: social, web and mobile signals import whatever inequality is already encoded in them, and the address inference product extends the modelling to where a person lives.
The individual's position differs by regime rather than by vendor choice: a Crisil characterised borrower is a company with advisers, while a Trusting Social scored consumer may live in a market affording no explanation right, with no published notification, access or challenge route. Neither vendor publishes fairness testing, residency or subprocessor detail, and Trusting Social's institutional customer count has declined from a previously reported 170, worth asking about directly.