Singapore — ANewswire — May 12, 2026 - In this exclusive interview with ANewswire, Kevin, CEO and Founder of TrustPlus AI, shares the journey behind building an emerging AI innovator in financial services. Following the company’s recognition at Money 20/20, Kevin discusses TrustPlus AI’s mission to transform credit risk management through intelligent workflow solutions, the growing role of AI in fintech, and the company’s ongoing focus on serving global financial institutions for global.

The Origin Story & Founding Vision

Kevin, congratulations on TrustPlus AI’s feature at Money20/20 — a huge milestone. To start from the beginning, what inspired you to found TrustPlus AI?

Thank you. Money20/20 Asia was an important milestone for us because it brought together exactly the community we built TrustPlus AI to serve: financial institutions, payment companies, fintech leaders, and risk professionals who are rethinking how financial services should operate in the AI era.

The inspiration for TrustPlus AI came from more than 20 years of working in credit risk across global institutions, including PayPal, Wells Fargo, GE Capital, and credit insurance businesses in Asia. Across different countries and institutions, I kept seeing the same pattern: highly trained credit professionals were spending most of their time preparing data rather than applying judgment.

Commercial underwriting is supposed to be a judgment-led discipline. But in practice, analysts often spend nights gathering documents, spreading financial statements, scanning adverse media, and formatting credit memos. By the time they reach the actual risk decision, they are already exhausted. That is not a technology problem alone. It is a workflow problem.

What specific problem in the market did you identify that made you say, “This has to be solved — and AI is the answer”?

The problem was what I call the “judgment deficit” in credit risk.

The credit risk industry does not have a lack of data. In fact, financial institutions have more data than ever: financial statements, transaction histories, web data, adverse media, industry reports, KYB information, ESG signals, and portfolio performance data. The problem is that this information is fragmented, manual to process, and difficult to convert into consistent credit judgment at scale.

Underwriters are not hired to copy numbers from PDFs into spreadsheets. They are hired to assess risk. But traditional workflows force them to spend the majority of their time on preparation work: collecting documents, spreading financials, researching companies, and drafting memos.

AI is the answer only if it is applied to the workflow, not just as a chatbot or document extraction tool. TrustPlus AI was created to automate the preparation layer of underwriting so human experts can focus on review, judgment, and approval. The goal is not to replace credit professionals. The goal is to give them back the time and clarity needed to make better decisions.

Was there a “eureka moment” or was the founding of TrustPlus AI a gradual evolution of ideas? Walk us through that journey.

It was both. The idea developed over many years, but there was a clear moment when it became impossible to ignore.

One night in 2024, I saw underwriting teams still working late at night, manually fixing financial spreading errors and preparing credit files just like what I did 15 years ago. These were highly capable professionals, but the process was forcing them to spend their best hours on repetitive preparation work. That moment crystallized the problem for me.

I had seen the same issue throughout my career. At global banks, payment companies, and credit businesses, the technology improved around the edges, but the underwriting workflow itself had not fundamentally changed. Analysts still moved between spreadsheets, PDFs, web searches, internal templates, emails, and approval processes.

That is when I decided TrustPlus AI needed to be built. The mission was not simply to make underwriting faster. It was to redesign what underwriters spend their time on.

Solving Real User Problems

Can you describe the core problems TrustPlus AI solves for its users today? Who benefits the most from your platform?

TrustPlus AI solves the manual workflow problem in commercial credit underwriting and portfolio monitoring.

Our users are financial institutions, including banks, payment companies, credit insurers, private credit funds, fintech lenders, and enterprise credit teams. The platform helps teams automate the most time-consuming parts of underwriting: financial spreading, KYB, credit research, business model analysis, adverse media review, credit memo generation, and portfolio monitoring.

The people who benefit most are credit analysts, risk managers, and senior credit approvers. Analysts gain time back from repetitive preparation work. Risk leaders gain consistency, transparency, and better portfolio visibility. Institutions gain the ability to scale underwriting without scaling headcount at the same pace.

How does your platform make users’ lives measurably better? Can you share any real-world outcomes, case studies, or metrics that demonstrate impact?

The impact is very measurable.

Based on client deployments, TrustPlus AI has demonstrated a 5-10x increase in review processing speed, a 3x acceleration in time to revenue, and an estimated 30% reduction in credit losses. In commercial underwriting, we reduce processing time from more than 24 hours to under one to three hours.

For specific workflow components, financial spreading can be completed in approximately twenty minutes with accuracy above 95%, while credit research and analysis can be completed in approximately thirty minutes.

But the most important metric is not only speed. It is the shift in how credit professionals spend their time. Traditionally, analysts may spend c80% of their time preparing data and only c20% applying judgment. We are trying to reverse that. AI should handle the preparation work so human experts can focus on the risks that matter.

Many AI products claim to solve problems but struggle with real-world adoption. How have you ensured that TrustPlus AI’s technology translates from a product demo into genuine, everyday user value?

We started with the real workflow, not the technology demo.

Many AI products look impressive in a controlled environment but fail when they meet enterprise complexity. TrustPlus AI was designed around live underwriting operations: real files, real templates, real approval processes, real compliance expectations, and real productivity constraints.

We also chose to start with a very demanding enterprise use case. Serving the global credit risk underwriting team of one of the world’s largest global payment companies forced us to build for scale, governance, and cross-border complexity from the beginning.

Our view is simple: if the product does not reduce manual work for analysts, improve consistency for managers, and preserve control for senior risk leaders, it will not be adopted. Adoption happens when the platform fits into how credit teams actually work.

Technology, AI & Innovation

Can you give us a non-technical overview of the core AI technologies powering TrustPlus AI? What makes your tech stack unique compared to competitors?

In simple terms, the platform reads and structures financial and business information, converts unstructured documents into usable credit data, retrieves relevant research and adverse media, generates analysis, and produces credit outputs that align with each institution’s workflow.

What makes our technology stack unique is that it is not just an AI layer. It is a credit workflow system. The AI is embedded inside the underwriting process, from document intake to memo generation and monitoring. That means the platform does not simply produce answers; it supports the full decision workflow with governance, evidence, and auditability.

Data privacy and regulatory compliance are critical. How does TrustPlus AI handle sensitive user data while remaining compliant with regional and international regulations like PDPA, GDPR, and others?

Data privacy and compliance are central to how we built the platform.

TrustPlus AI is designed for enterprise financial institutions, where sensitive commercial, financial, and customer data must be protected. We follow strong security and data governance practices, including controlled access, audit trails, secure infrastructure, and strict handling of client data.

We are SOC 2 certified and deployed on Microsoft Azure, which allows us to meet enterprise-grade security and compliance expectations. We also design our workflows to support regional and international privacy requirements, including GDPR, CCPA, and relevant data protection frameworks.

Our principle is that financial institutions must remain in control of their data. AI should improve workflow efficiency without compromising data security, privacy, or regulatory obligations.

Money20/20 Feature & Global Recognition

TrustPlus AI was featured at Money20/20, one of the world’s most prestigious fintech events. What did that experience mean to you personally, and what doors has it opened for the company?

Being selected as one of five featured startups at Money20/20 Asia was meaningful because it validated the problem we are solving.

Credit underwriting is not always the most visible part of fintech, but it is one of the most important. Behind every lending decision, merchant approval, credit limit, or portfolio exposure is a risk workflow that determines how capital is deployed.

Money20/20 gave us a platform to show that commercial credit risk workflows are ready for transformation. It also opened conversations with financial institutions, payment companies, investors, and ecosystem partners who recognize that AI should not only improve front-end customer experience, but also modernize the core risk infrastructure behind financial services.

Target Market & Business Strategy

Who is TrustPlus AI’s primary target market today — is it B2B, B2C, or B2B2C? Which industries are your primary focus?

TrustPlus AI is a B2B enterprise platform.

Our primary customers are financial institutions and enterprise credit teams. That includes banks, payment companies, fintech lenders, credit insurers, private credit funds, and companies with significant commercial credit exposure.

Our strongest focus today is commercial underwriting and portfolio monitoring, particularly in environments where institutions need to review many businesses across multiple markets, languages, industries, and risk profiles.

How do you think about competition? Who are the key players in your space, and what is TrustPlus AI’s sustainable competitive advantage?

There are many companies applying AI to financial services, including data providers, document processing platforms, credit scoring companies, and analytics vendors. Some large incumbents have strong data assets and are adding AI features.

Our advantage is different. We are focused on workflow.

TrustPlus AI was built by credit practitioners who understand how underwriting actually works: financial spreading, exception handling, risk appetite, memo structures, portfolio reviews, and institutional memory. We are not simply adding AI to a database. We are redesigning the operating environment for commercial credit teams.

Our sustainable advantage is the combination of domain expertise, end-to-end workflow design, modular customization, explainability, and enterprise-grade governance.

Future Vision & 5-Year Goals

Where do you envision TrustPlus AI in five years? What milestones or achievements would signal that the company has truly fulfilled its mission?

In five years, I want TrustPlus AI to be the standard workflow platform for commercial credit underwriting and portfolio monitoring across financial institutions.

The milestone is not just revenue or number of clients. The real milestone is changing how credit teams work. If underwriters no longer spend most of their time on manual preparation, if institutions can make faster and more consistent decisions, and if portfolio risk can be monitored more intelligently, then we will have fulfilled our mission.

Success means that our workflow intelligence platform becomes a trusted infrastructure layer for credit risk management, not a black-box replacement for human judgment.

What new products, features, or services are you building right now that you’re most excited about? Can you share any sneak peeks?

We are especially excited about expanding our merchant web intelligence and portfolio monitoring capabilities.

Financial institutions increasingly need a live view of business risk, not just a point-in-time underwriting memo. Merchant businesses can change quickly: websites change, products change, adverse media emerges, regulatory signals appear, and business models evolve.

Our merchant web intelligence solution is designed to help institutions monitor business activity, identify risk signals, and integrate those insights into underwriting and portfolio review workflows. This is a natural extension of our core platform because underwriting should not end at approval. Risk management is continuous.

Leadership, Culture & Team

Building an AI company requires rare talent. How do you attract and retain top AI researchers and engineers in Singapore’s competitive tech talent landscape?

We attract people by giving them a meaningful problem to solve.

Many engineers can work on interesting AI products. But at TrustPlus AI, the work has a direct impact on how financial institutions make risk decisions, allocate capital, and support business growth. That is a serious mission.

We also combine technical talent with domain expertise. Our engineers work closely with credit professionals, so they are not building in isolation. They see how the product is used, why the details matter, and how their work changes the daily lives of underwriters and risk teams.

To retain talent, we focus on ownership, speed, learning, and mission. People want to build something that matters.

Rapid-Fire Closing Round

One word to describe the future of AI:

Accountable.

The one book, podcast, or resource every AI entrepreneur should consume:

The Lean Startup by Eric Ries, because AI founders need to learn quickly, validate with real users, and avoid building technology that does not solve a real problem.

The biggest myth about AI that you’d like to debunk:

That AI replaces experts. In complex fields like credit risk, AI should amplify experts by removing repetitive preparation work and giving them better information for judgment.

The one thing regulators get wrong about AI:

Sometimes AI is treated as one category. But there is a major difference between uncontrolled black-box automation and governed, explainable, human-in-the-loop AI used inside enterprise workflows.

Your message to the next generation of AI founders:

Build for real problems, not headlines. The financial industry has some of the world’s most complex and important business challenges. If you solve them with trust, rigor, and ambition, your company can compete globally.