Monnai vs Trusting Social (2026)
An aggregation layer against a scoring incumbent, both built on the premise that people without files can be read from the signals around them. Monnai normalizes payment, communication, device and identity data across the United States, Latin America, India and Southeast Asia into four decisioning modules behind one interface, on the argument that contextualizing incompatible sources across borders is the hard problem. Trusting Social has spent thirteen years scoring consumers from social, web and mobile data across Vietnam, Indonesia, India and the Philippines, with over a billion people scored and six of the ten largest Philippine banks named as clients. Their evidence fails in opposite directions. Monnai publishes the numbers and names nobody: 99 percent fraudulent identity detection, approvals up 40 percent and defaults down 45 percent, all self reported, with an insight count that moves between sources unexplained. Trusting Social names the penetration and publishes no numbers. No accuracy, lift or validation figure appears anywhere, while the parent's publicly filed accounts make its finances readable. What the two share should shape any diligence. Neither identifies a single upstream data source despite sourcing being the whole business, and neither describes what a scored person is told or can contest. Both serve competing lenders from a shared intelligence layer with the boundary unstated.
- Your operation spans four regions and a dozen data regimes. One interface returns hundreds of insights across identification, fraud, credit and collections, with aggregation and normalization handled for you.
- Your case needs both error directions. Published figures cover 99 percent fraudulent identity detection alongside approvals up 40 percent and defaults down 45 percent, the two sided shape that distinguishes improvement from loosened standards.
- Your analysts investigate visually. A graph based dashboard surfaces risk factors in a single view, built to collapse manual investigation time.
- Your market is Southeast Asian consumer lending at national scale. Over a billion consumers scored, six of the ten largest Philippine banks named as clients, and more than 40 institutions in Vietnam.
- Your committee weighs regulator engagement. The Reserve Bank of India admitted the company to a supervised program and Visa to its Fintech Partner Connect program, with information security certified to ISO 27001.
- Your diligence reads accounts, not decks. The parent files public financials, so revenue, losses and cost structure are verifiable directly.
This comparison is published by AI FinTech Index, an independent research platform that publishes independent ratings of AI vendors for financial services. Monnai 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
| Monnai | Trusting Social | |
|---|---|---|
| Primary category | Credit Decisioning & Underwriting | Credit Decisioning & Underwriting |
| Founded | 2021 | 2013 |
| Headquarters | San Francisco, California, United States | Singapore |
| Website | www.monnai.com | trustingsocial.com |
Side by Side
Select any grade to read the note behind it.
| Axis | M Monnai |
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
Monnai
Monnai aggregates the signals around a person into decisions. It normalizes payment, communication, device and identity data across the United States, Latin America, India and Southeast Asia into four decisioning modules behind one interface, on the argument that contextualizing incompatible sources across borders is the hard problem of alternative data. According to the AI FinTech Index, its evidence is numbers without names: 99 percent fraudulent identity detection, approvals up 40 percent and defaults down 45 percent, all self reported with no baseline or validation. No customer is named anywhere, and a published insight count moves from over 350 to over 500 to over 1,000 across sources without explanation. The silence that matters most for an aggregator is the supply chain. Not a single upstream source, bureau, telecommunications partner or platform is identified, so a buyer cannot assess licensing or what happens to coverage when a platform restricts access. The scored person has no described notification, access or correction anywhere.
Source: AI FinTech Index, 2026
Trusting Social
Trusting Social scores consumers the file system cannot see. It has thirteen years of scoring from social, web and mobile data across Vietnam, Indonesia, India and the Philippines, over a billion people scored, six of the ten largest Philippine banks named as clients and admission to a Reserve Bank of India program. Its parent's publicly filed accounts make its finances readable. According to the AI FinTech Index, the penetration is named and the performance is not: no accuracy, lift or validation figure is published anywhere in thirteen years for a product whose output is a prediction about a person. The aggregator silences are there too. No upstream data source is identified, and the scored consumer has no notification, access or challenge route. Cross client learning is unstated where clients compete for the same borrowers, and an institutional customer count has declined from a previously reported 170.
Source: AI FinTech Index, 2026
Common questions
How do Monnai and Trusting Social differ?
They overlap in Southeast Asia and diverge in shape. Monnai is an aggregation layer returning insights across four decisioning modules and four regions including the United States and Latin America, while Trusting Social is a scoring incumbent concentrated in four Asian markets with named bank penetration. 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's evidence is more verifiable?
Trusting Social, whose six of the ten largest Philippine banks, Reserve Bank of India program admission and publicly filed parent accounts are all checkable. Monnai names no customer in any market, against the named penetration on the other side. 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.
Do these vendors name their data sources?
Neither names one. Both describe input categories, payment, communication, device and identity at Monnai, social, web and mobile at Trusting Social, and identify no upstream provider, which for aggregation businesses is the disclosure that determines coverage durability. 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 does each leave unexplained?
Monnai publishes no baseline or sample behind its outcome figures and no explanation of the moving insight count. Trusting Social publishes no accuracy or lift figure and no reason its institutional count declined. 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 Fraud Detection & Transaction Risk page.
Both vendors assess people who are entirely outside the relationship, on signals those people never thought were financial, and neither describes notification, access or correction anywhere in four regions of operation each. The supply chain silence is the material one for both, because aggregation is the product: not a single upstream source, bureau, telecommunications partner or platform is identified by either, so a buyer cannot assess licensing, durability or what happens to coverage when a major platform restricts access, which has occurred repeatedly in this data category.
The evidence fails in opposite directions. Monnai's outcome figures carry no baseline, sample or validation, and its published insight count moves from over 350 to over 500 to over 1,000 across sources without explanation. Trusting Social's scale and penetration are named and its model performance is not, with no accuracy figure published in thirteen years and an institutional customer count that has declined from a previously reported 170. Neither publishes residency in markets with localization requirements, and both leave the cross client learning boundary unstated where clients compete for the same borrowers.