Agenly for Banks: Using Agentic AI to Route Customers to the Right Resolution Path

Early arrears is not one uniform problem. Some customers have missed a single payment and need a clear way to act. Others may be experiencing affordability pressure and require flexibility or specialist support.  Treating every case through the same agent-led queue increases cost. Applying the same automated journey to every customer creates a different risk: straightforward cases receive too much manual attention, while sensitive cases may not receive enough. 

The opportunity for banks is to identify the
right resolution path earlier.

Agenly is a cloud-native SaaS Agentic AI platform for financial resolution, designed to engage customers through messaging and guide them towards bank-approved repayment options. It acts as AI collections software that helps banks route straightforward arrears towards self-service, while identifying cases that need greater flexibility or human support. That requires more than a basic chatbot.or a generic use of artificial intelligence in banking. Digital resolution depends on connected conversations, policy-driven workflows and payment enablement.

The following use cases demonstrate how agentic AI for financial services can work in practice.

#1 Use Case: Early-Arrears Self-Service for Loans and Cards

A customer misses a credit-card payment or loan instalment. The account is only one payment late, so the bank wants to act quickly without pushing every case into an agent-led collections queue.

The challenge: simple missed payments become expensive manual work 

In the traditional model, the customer may receive a letter, an outbound call or a generic reminder. This creates delay, depends on call-centre capacity and may reach the customer at the wrong time. It also places simple payment issues in the same queue as cases that genuinely require human judgement.

Early arrears is often the best time to intervene. The case is still relatively simple, the customer may remain reachable and the bank can resolve a missed payment before it requires repeated calls and manual follow-up.

Yet many collections strategies remain heavily dependent on traditional outreach, even though customers appear more likely to act through digital channels. One study found that digital contact led to payment action in 73% of cases, while text messages generated a 77% action rate compared with 48% for telephone calls (McKinsey, 2019).

The implication is clear: Banks should not spend expensive agent time on cases that can self-resolve.

The business case extends beyond engagement. Digital-first collections programmes have been associated with at least 15% lower collection costs, greater self-service and 12% more payments among digitally contacted customers (McKinsey, 2021). Finalta benchmarks cited by McKinsey also suggest that up to 48% of incoming US contact-centre calls could potentially be redirected to digital resolution, highlighting the scope to move straightforward interactions away from the call centre (McKinsey, 2020).

The Solution: A digital-first path to early resolution 

Agenly turns the first arrears contact into a guided digital journey. An eligible case passes from the bank’s servicing or collections system into Agenly. The customer receives a personalised SMS or Viber message, can ask simple questions and is guided towards the options allowed by the bank’s rules:

• pay now
• choose a payment date
• confirm a promise to pay
• request a repayment arrangement
• escalate to a human agent

Straightforward cases can self-resolve, while agents focus on disputes, vulnerability and more complex hardship.

How it works: From overdue account to recorded outcome 

1. Trigger: The account becomes overdue or is identified as at risk, and the case enters Agenly from the bank’s system.

2. Engage: Agenly sends a personalised message through an approved channel such as SMS or Viber.

3. Resolve: The AI agent explains the balance, answers simple questions, captures customer intent and guides the customer towards payment, a promise to pay or an eligible arrangement.
   

4. Synchronise: Payment commitments, outcomes, customer requests and escalations are written back to the bank’s collections, servicing or CRM environment with a clear audit trail.

    

What to Expect: Lower manual effort, faster customer action

Agenly helps banks turn the first missed-payment contact into a private, guided digital journey where the customer can pay, commit, request an arrangement or ask for help without waiting for the call centre.

#2 Use Case: Hardship-Aware Repayment Arrangements

A customer has missed more than one payment, asks for additional time or uses language that suggests affordability pressure. 

The challenge: Customers who need support do not always ask for it 

This is no longer a simple reminder case. The bank needs to understand whether the customer can recover quickly, requires a structured repayment option or should be routed to specialist support. Handling every case manually creates backlog and inconsistent treatment. Handling every case through generic automation can feel insensitive and create conduct risk.  

The scale of the challenge is significant. In January 2024, 7.4 million UK adults felt heavily burdened by bills and credit commitments, while 5.5 million had fallen behind on or missed bills or credit commitments during the previous six months. Yet only 29% of adults who had fallen behind sought help (FCA, 2024 ). 

Customer circumstances must also shape the response. UK rules require firms to treat customers in or approaching arrears with forbearance and due consideration, taking their individual circumstances into account (FCA, CONC 7.3). At European level, regulatory guidance similarly emphasises customer engagement, assistance and the documentation of customer dealings (EBA, Guidelines on arrears and foreclosure).

Banks therefore need a scalable way to encourage earlier engagement, gather relevant information consistently and distinguish customers who can self-cure from those who require flexibility or specialist support.

The Solution: A Controlled Digital Path for Financial Hardship Support

Agenly gives customers a clear, respectful way to explain their situation. After identifying intent, the AI agent is trained and configured to recognise vulnerability signals and assess the severity of potential customer harm, including FCA-relevant drivers such as health, life events, financial resilience and capability.

The journey then routes the customer through bank-defined treatment paths: a short extension for temporary pressure, a payment freeze or human-in-the-loop review when the case shows higher vulnerability, affordability concerns or the need for specialist support. AI supports early identification, triage and guided disclosure, but does not make autonomous financial decisions. The bank remains in control of eligibility rules, options, escalation thresholds and final outcomes.

How it works: From hardship signal to governed escalation

1. 1. Detect: The customer misses a payment, asks for help or uses language that suggests affordability pressure, vulnerability or personal difficulty.

   2. Assess: Agenly gathers relevant details through guided questions and keeps the conversation within bank-defined policy boundaries.

   3. Recommend: The journey presents only eligible outcomes, such as a short extension, repayment plan, promise to pay, request for review or human specialist support.

   4. Govern: Sensitive, disputed, low-confidence or vulnerable-customer cases are escalated with conversation context, captured intent and audit history.

      
      

What to expect: More consistent triage, more accessible support 

Agenly helps banks scale empathetic support by using AI to listen, structure the conversation and guide the customer towards a bank-approved next step without removing human oversight.

One Digital Front Door, Two Resolution Paths 

A missed payment and financial hardship should not follow the same journey. Agenly helps banks distinguish between customers who can self-resolve, those who need greater flexibility and cases that require human support.

The goal is not automation at any cost. It is earlier, more appropriate resolution. This reflects a wider shift in customer resolution: from call-centre-heavy, manual processes towards AI-assisted, governed digital journeys and agentic AI for banks.

Ready to rethink early-arrears resolution?