How Thailand’s Banks Can Manage Rising NPLs with Scalable Collections

Thailand’s banks face a difficult collections equation. Nonperforming loan volumes (NPLs) remain under pressure, household finances are strained, and lending growth has slowed. At the same time, collections leaders are expected to control operating costs, protect customer relationships, and meet increasingly demanding governance standards.

Adding more collectors may provide temporary capacity, but it doesn’t solve the underlying problem. Manual processes, fragmented data, static work queues, and inconsistent treatment strategies will continue to limit performance, regardless of team size. Banks need a different approach.

Automation in collections can help institutions manage larger and more complex delinquent portfolios without increasing headcount at the same rate. By combining connected data, workflow automation, machine learning, and human judgment, collections teams can focus their effort where it has the greatest impact.

The result isn’t simply a more efficient recovery operation. It’s a more responsive way to support customers through financial difficulty while protecting asset quality and operational resilience.

Why are rising NPLs creating pressure for Thailand’s banks?

Thailand’s banking system remains well capitalized, but asset quality pressure continues to shape lending and collections strategies.

The Bank of Thailand reported gross nonperforming loans of approximately THB 554.9 billion in the second quarter of 2025. Overall banking system loan growth contracted by 0.9% year over year, with consumer and SME lending continuing to shrink amid elevated credit risk. Loan growth contracted again by 1% year over year in the third quarter of 2025. These conditions place collections teams in a central role.

When banks grow loan books more cautiously, the performance of existing accounts becomes even more important. Preventing roll rates, improving cure outcomes, and managing repayment arrangements can have a direct effect on profitability and portfolio stability.

Collections teams also face a more varied customer population than headline NPL figures can show. Some customers are experiencing temporary cash flow disruption. Others may have several products with the same institution, including personal loans, vehicle finance, cards, and small business facilities. A customer who can resolve an overdue payment with an early reminder shouldn’t receive the same treatment as someone facing sustained hardship.

Without detailed segmentation, growing volumes can quickly lead to generalized treatment strategies. Collectors work through large account lists, contact attempts increase, and complex cases compete with straightforward ones for attention. The bank becomes busier without becoming more effective.

Why adding more collectors isn’t a long term NPL strategy

Increasing collections headcount can appear to be the most direct response to rising delinquency. More accounts seem to require more people. In practice, this approach has limits. New collectors require recruitment, training, management, system access, and quality assurance. They also inherit the same process weaknesses as the existing team.

If customer information remains spread across several systems, each collector still has to search for context. If work allocation is based mainly on days past due or outstanding balance, teams may still spend time on accounts likely to self cure while customers needing urgent support wait. Manual expansion can also create inconsistency.

Two customers with similar circumstances may receive different treatment depending on which collector handles the account. Promises to pay may be followed up unevenly. Contact frequency and channel use may vary between teams or product areas

As volumes increase, managers have to devote more time to monitoring individual performance rather than improving portfolio strategy.

A scalable collections model changes the relationship between account volume and operational effort. It uses automation for repeatable activity, machine learning for prioritization, and human expertise for cases requiring judgment or the human touch. Headcount remains important, but it’s no longer the main source of additional capacity.

What does scalable NPL management look like for Thai banks?

Scalable nonperforming loans management in Thailand begins with a unified view of customers, accounts, and activity. A bank may hold data across core banking, loan servicing, vehicle finance, cards, customer relationship management, payments, digital channels, and credit bureau connections. Each system may be valuable, but collectors need the relevant information presented in one operational environment.

A configurable solution can bring these sources together without forcing the bank to replace its wider technology estate. Collectors can see payment history, previous contacts, promises, arrangements, related products, and approved indicators of vulnerability or risk. Managers can apply consistent policies across portfolios while retaining different strategies for each product or customer group.

Once the data is connected, the bank can automate a significant amount of routine work. Examples include:

• Sending reminders before or after a missed payment
• Assigning accounts to the right queue
• Recording and monitoring promises to pay
• Triggering follow ups after a broken arrangement
• Offering eligible self service options
• Escalating sensitive or complex cases
• Stopping communications after payment or resolution
• Applying contact and compliance rules consistently

Automation prevents collectors from spending time on administrative activity when a system can complete it reliably. The team gains more capacity to understand customer circumstances, negotiate sustainable arrangements, and manage cases where a standardized journey isn’t appropriate.

How can machine learning help manage rising NPLs?

For Thailand banks, machine learning is a valuable way to adopt AI in collections. Machine learning analyzes historic and current account data to identify patterns associated with likely outcomes. It can help a bank estimate which customers may self cure, which accounts are likely to respond to a reminder, and which cases have a higher risk of progressing into deeper delinquency.

These predictions can improve prioritization across thousands or millions of accounts. Instead of allocating work based only on balance or delinquency stage, a bank can consider several factors at once:

• Likelihood of payment
• Risk of further deterioration
• Previous response to contact
• Promise to pay performance
• Product type and exposure
• Preferred channel
• Customer vulnerability indicators
• Eligibility for restructuring or support

A model score can feed a controlled strategy combining predictive insight with bank policy and regulatory requirements. For example, a customer with a high probability of self cure may receive a low intensity digital reminder. Someone showing early signs of stress could receive proactive support before the account moves deeper into delinquency.

A higher risk case may be directed to an experienced collector with a complete account history and an approved set of treatment options. Machine learning helps the bank use its existing resources more intelligently. Collectors spend less time deciding which account to handle next and more time working on cases where their involvement can change the outcome.

How can banks integrate collections with existing modernization programs?

Many Thai banks are already investing in core modernization, data platforms, cloud infrastructure, analytics, personalization, payments, and fraud prevention. Collections technology should complement these programs rather than compete with them.

A configurable collections solution can sit alongside existing banking systems and use secure integration methods based on the institution’s architecture. These may include APIs, event messages, service calls, or scheduled data exchange.

This means a bank doesn’t have to wait for a complete core transformation before improving collections. The collections solution can provide a specialized layer for:

• Account segmentation
• Work prioritization
• Treatment strategy
• Workflow orchestration
• Promise management
• Digital engagement
• Compliance controls
• Performance reporting

As underlying systems change, integrations can be updated without rebuilding every collections process from the beginning. A specialized solution also helps prevent business rules from becoming embedded across several applications. Collections logic can be managed in one governed environment, making policy changes faster and easier to audit.

This becomes particularly valuable after mergers, acquisitions, or portfolio expansion. Different lending businesses may use separate systems, definitions, and workflows. A unified collections environment can provide consistent group level governance while enabling treatment strategies to reflect the characteristics of each portfolio.

How can banks protect customer trust while increasing collections capacity?

Efficiency can’t come at the expense of customer treatment. Customers experiencing financial difficulty may already feel uncertain or overwhelmed. Repeated calls, conflicting messages, and unsuitable payment demands can reduce engagement rather than improve it.

A scalable strategy should increase relevance rather than pressure. The bank can use data and machine learning to decide when human support is needed and when a simple digital interaction is sufficient. It can also coordinate communications so customers aren’t contacted again after completing an action through another channel.

A customer centric journey might begin with a reminder before a due date. If payment is missed, the customer could receive a secure link offering approved payment or support options.

A customer showing signs of more serious hardship can be routed to a trained collector. The agent can see previous interactions and available treatments without asking the customer to repeat the full story. Customers who resolve their account through self service don’t need unnecessary follow up. Those requiring more support receive human attention sooner.

Creating more capacity without creating a larger team

Rising NPL volumes don’t automatically require a proportional increase in collectors. Banks can create operational capacity by reducing manual work, prioritizing accounts more accurately, and giving customers practical ways to resolve straightforward issues through digital channels. The key is to apply technology selectively.

Automation should handle repeatable activity. Machine learning should help identify risk and likely outcomes. Human collectors should focus on conversations and decisions requiring context, flexibility, and care.

A phased modernization program can begin with a specific portfolio or delinquency stage. The bank can connect data, standardize workflows, and introduce prioritized work queues before adding predictive models and more advanced strategies.

Each phase should produce measurable results, such as reduced manual activity, faster case resolution, improved arrangement performance, or lower progression into later delinquency.

Frequently asked questions

Why are NPLs rising in Thailand?

NPL pressure is being driven by household financial stress, cautious lending conditions, and continued risk across consumer and SME portfolios. Slower loan growth also makes the performance of existing accounts more important to bank profitability.

How can Thai banks manage rising NPLs without hiring more collectors?

Banks can automate routine activity, prioritize accounts using machine learning, and provide self service options for suitable customers. This lets collectors focus on complex cases where human judgment and customer support are most valuable.

What is NPL management in Thailand?

NPL management covers the identification, treatment, restructuring, and recovery of delinquent loans. It includes early intervention, customer communications, payment arrangements, promise monitoring, escalation, and reporting.

Can a collections solution integrate with existing core banking systems?

Yes. A configurable collections solution can connect with core banking, loan servicing, payment, credit bureau, and analytics systems through APIs, messages, services, or scheduled data exchanges.

Does AI replace human collectors?

No. Machine learning can handle account scoring, prioritization, routing, and repetitive administrative work. Collectors remain essential for sensitive conversations, negotiation, hardship support, and complex resolutions.

How can banks govern machine learning in collections?

Banks can govern machine learning by using approved data, documenting model logic, monitoring performance, maintaining audit trails, and retaining human oversight. Models should also be reviewed for drift and unexpected differences in customer outcomes.

Supporting NPL management with C&R Software

Thailand’s banks need a collections model capable of handling more accounts without adding cost and complexity at the same rate.

C&R Software’s Debt Manager is a configurable collections and recovery solution designed to manage activity across the debt lifecycle. It can integrate with existing core, lending, analytics, and machine learning environments while supporting automated workflows, prioritized queues, customer level strategies, and consistent governance.

The system can be deployed within an architecture aligned with an institution’s data, security, and regulatory requirements.

Together, Debt Manager and FitLogic help banks respond to rising NPLs with greater control, consistency, and scalability while keeping human support at the center of complex customer journeys. Learn more by reaching out to inquiries@crsoftware.com.