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04 AUGUST 26

Recovery Rate Improvement: How TBN’s AI Turns Thai Debt Collection into Revenue

TBN Corporation is using AI to achieve Recovery Rate Improvement for Thai financial institutions transforming Debt Collection from a back-office cost centre into a Profit Center
TBN AI Debt Collection-mobile with bill and credit card

Recovery Rate Improvement

Recovery Rate Improvement is at the heart of TBN Corporation's new AI strategy for Thai financial institutions — moving Debt Collection from a back-office cost centre to a revenue-generating Profit Center. The goal is clear: raise Recovery Rates and reduce NPL across the financial system. According to TBN, "debt is no longer just a liability. It is an opportunity."

TBN's Strategy for Recovery Rate Improvement

TBN's approach transforms debt management into an intelligent, data-driven operation. Rather than treating collections as a reactive, manual process, the company is using AI to identify the right accounts, at the right time, through the right channel.

The system is built as part of TBN's End-to-End Lending Solution — connecting every stage of the credit lifecycle from origination through to collections and NPA management on a single platform.

The Problem TBN Sees in Thai Finance

Panayu Sirikrachangsri, CEO of TBN Corporation, explained the company's view directly:

"Thailand's economy remains trapped in an income ceiling while household debt continues to expand. The challenge for the financial industry is no longer just how to lend more — it is how to manage credit and collect debt efficiently. The real goal is to maximise Recovery Rates while reducing NPL across Thailand's financial system."

This diagnosis shapes how TBN has built its AI Debt Collection capabilities.

How AI Drives Recovery Rate Improvement

TBN's system operates across three layers of intelligence — each contributing directly to Recovery Rate Improvement across the financial institution's portfolio.

AI-Powered Behavioural Scoring

Debtor data is analysed continuously — payment history, response patterns, and behavioural indicators — to segment accounts by risk profile and likelihood of recovery. Collection teams receive a prioritised queue rather than a flat list. As a result, effort is directed toward accounts with the highest probability of success.

AI Interactive Voice Bot

An AI-driven voice system contacts customers directly and recommends the most effective collection approach based on real-time debtor behaviour. This extends the reach of collection teams without proportionally increasing headcount.

AI Early Detection and Prediction

Early warning signals are identified automatically — changes in payment patterns, account behaviour, and risk indicators — allowing financial institutions to intervene before a loan deteriorates.

Together, these three layers move Debt Collection from a reactive process to a proactive one.

TBN's Repositioning: From IT Vendor to Financial Data Infrastructure

Consequently, TBN is no longer positioning itself as a software development company. Instead, the company is becoming data infrastructure for the financial industry.

In practice, the core idea is to transform large volumes of financial data into decision-making engines for financial institutions — so that debt management evolves from an operational tool into a knowledge centre that shapes the competitive direction of the entire industry.

Global Data on Recovery Rate Improvement

TBN's strategic direction is consistent with global financial industry trends.

The market for Credit Risk, Debt Management, and Collection software is growing at more than 10% per year on average. Furthermore, leading banks in the United States and Europe now allocate 15-25% of their total IT budgets to AI investment.

The direct result is measurable Recovery Rate Improvement — AI has been shown to raise recovery by more than 15% while reducing NPL by 10-20%.

These figures confirm that AI Debt Collection is no longer a productivity enhancement — it is becoming a genuine competitive advantage in the financial industry.

Revenue Targets Built on Recovery Rate Improvement

TBN currently provides Debt Collection systems to leading financial institutions and debt management companies across Thailand. The company is also actively bidding with financial institutions and Non-Bank businesses in the rapidly growing consumer credit and digital lending segments.

TBN has set a target of approximately THB 200 million in Debt Collection service revenue over the next 3-5 years, with approximately THB 60 million expected to be recognised in the current year.

As Panayu Sirikrachangsri noted: "We do not see the debt management system as simply a collection tool. It is data infrastructure that reflects the reality of the financial system. If financial institutions can understand this data correctly, Debt Collection will no longer be the end of a credit journey — it will become one of the key mechanisms that shapes the future direction of the financial industry."

For more information about TBN's End-to-End Lending Solution or to explore Low-Code Banking for Thai financial institutions, visit the links above.

FAQ: Recovery Rate Improvement and TBN AI

Q: How is TBN's AI Debt Collection different from traditional collection systems?

A: The system uses AI to analyse debtor data, contacts customers via an AI Interactive Voice Bot, and predicts risk early through AI Early Detection. This replaces the traditional approach of manual, undifferentiated outreach that underutilises available resources.

Q: What does TBN's End-to-End Lending Solution cover?

A: It covers the full credit lifecycle — from loan origination and risk assessment through credit quality monitoring, debt management, and NPL/NPA management — all available from TBN.

Q: How much can AI reduce NPL in financial institutions?

A: Data from leading banks in the United States and Europe shows that AI investment has reduced NPL by more than 10-20% while increasing Recovery Rates by more than 15%.

Source: Compiled from an interview with Panayu Sirikrachangsri, CEO of TBN Corporation, by Brand Inside

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