Why Data and AI-Enabled Collections Belong in Your Strategy

• Collections is one of the most measurable AI investments for banks and credit unions.
• AI helps collectors act faster by turning account data into actionable insights.
• Predictive intelligence enables earlier risk detection and smarter resource allocation.
• Data, analytics, and AI help financial institutions improve both operational efficiency and portfolio performance.

As banks and credit unions build their 2027 technology budgets, a conversation around AI and data is guaranteed. With countless potential applications, the challenge is not where these technologies can be used, but where they can deliver the fastest and most measurable impacts on operations, productivity, and business results.

Jack Henry’s 2026 Strategy Benchmark found that 88% of financial institutions expect to increase technology spending over the next two years, with AI ranked as the top planned investment by 48% of respondents. FIs are working to prioritize digital engagement, data analytics, and AI, while collections teams are confronting growing account volumes, rising expectations for digital self-service, and the need to do more with existing or declining resources.

Taken together, these technology priorities and operational pressures make collections a compelling starting point for AI and data investment. It is information-intensive, operationally demanding, compliance-sensitive, and directly connected to the account-holder experience. It also produces measurable signals through payment behavior, delinquency events, engagement activity, promises, payments, and resolution results.

One of the first places institutions see measurable value from AI is through capacity expansion. Most financial institutions are not struggling with a shortage of data. They are struggling with a shortage of time. Collectors already have access to account history, payment activity, notes, statuses, promises, policies, and prior actions. The challenge is turning all of that information into a clear next step quickly enough to support daily work.

Something as simple as an AI-driven account summary combines key account information so collectors can quickly understand what has happened, why it matters, and what action may be appropriate next. In early use cases, collectors using AI-generated summaries can complete account research up to 90% faster than those relying on manual review. Standard account reviews can be completed in five to seven seconds instead of more than a few minutes spent piecing together information from multiple sources.

The impact becomes even greater in specialty scenarios such as bankruptcy. What once took approximately 2.5 minutes to review can now be summarized and consumed in 25 to 35 seconds, reducing review time by roughly 75% on every case.

For institutions building 2027 budgets, this matters because productivity gains compound. When collectors spend less time searching for information, they can spend more time resolving delinquency, applying judgment, and protecting portfolio performance.

Collections teams sit on one of the richest sources of behavioral data inside a financial institution. Every day, they generate signals that help explain not only what has happened, but what is likely to happen next. These signals include payment activity, delinquency progression, promises-to-pay, communication history, recoveries, collector notes, and resolution outcomes. Together, they create a detailed picture of borrower behavior and portfolio risk.

The strongest collections intelligence strategies combine several types of data.

• Portfolio data helps institutions understand performance by delinquency stage, product, segment, or loan type.
• Industry data provides broader market context, helping leaders determine whether trends are unique to their institution or part of larger economic shifts.
• Benchmarking data helps organizations understand how their results compare with peers.
• Predictive models help identify patterns and risk signals before they appear in traditional reporting.

Individually, each data source provides insight. Together, they create intelligence.

For example, portfolio data may show that delinquency is increasing in a particular segment. Industry data may confirm whether peers are experiencing the same trend. Benchmarking data may identify whether performance is above or below market averages. Predictive models can then help determine which accounts are most likely to deteriorate further and where resources should be focused first.

This is where AI becomes especially valuable. Rather than forcing collections teams to manually interpret thousands of data points, AI can help surface patterns, highlight opportunities, and translate information into actionable recommendations. Data explains what is happening. AI helps institutions decide what to do about it.

The Severity of Delinquency model is a clear example of how data can make collections strategy more targeted. Instead of prioritizing accounts only by days past due, teams can use predictive intelligence to identify which accounts are more likely to become severely delinquent and which are more likely to resolve on their own.

Traditional collections strategies often assume that accounts in the same delinquency bucket carry similar levels of risk. In practice, that is rarely the case. Two accounts that are both 20 days past due may have very different probabilities of curing, rolling forward, or ultimately becoming a loss. Predictive models help identify those differences earlier, allowing collections teams to make better decisions about where to focus their time and effort.

By combining historical delinquency outcomes with behavioral and portfolio data, institutions can move beyond a one-size-fits-all collections approach. Higher-risk accounts can be prioritized for earlier intervention, while lower-risk accounts can remain on an automated path or receive less intensive treatment. The result is a strategy that aligns resources to risk rather than treating every delinquent account the same.

For collections leaders, the value is straightforward: collectors spend more time working accounts where intervention can influence the outcome and less time on accounts likely to self-cure. That becomes especially important during periods of rising delinquency, staffing shortages, or increased portfolio pressure, when every collector interaction matters.

The goal is not to replace strategy. It is to make the strategy more precise. By helping institutions identify risk sooner and prioritize resources more effectively, predictive intelligence enables a more proactive approach to collections management.  

Collections decisions are shaped by policies, procedures, collector notes, internal terminology, and years of institutional experience. That knowledge is valuable, but it is often hard to access consistently. AI changes the value of that knowledge by making it easier to surface in context.

One of the more interesting lessons from early AI implementations is that AI can learn how an institution actually works. It can recognize internal abbreviations, collections terminology, policy language, and account-level context. That makes the output feel less generic and more aligned to the way collectors already work.

The effectiveness of AI depends heavily on the quality of the information, procedures, and institutional guidance informing it. For that reason, AI readiness is not only about selecting a tool. It is also about making sure policies, procedures, and operational knowledge are current, accessible, and usable.

Many AI initiatives struggle to establish a clear return on investment. Collections is different because the work is operational, data-rich, and outcome-based.

Leaders can measure reductions in account research time, improvements in collector productivity, greater consistency in policy application, earlier risk identification, queue prioritization, and changes in portfolio performance. The connection between data, AI activity, and business outcome is easier to see because collections already works against measurable goals.

One institution using a Severity of Delinquency model recently provided an early example of this impact. Despite operating short-staffed for nearly two months, the collections team continued working through its queues because accounts were prioritized more effectively. At the same time, the institution reported its lowest delinquency levels since 2021. Predictive intelligence can help collections teams maintain performance even when resources are constrained.

Collections data also has value beyond collections performance. Payment behavior, delinquency progression, cure rates, recoveries, and resolution outcomes can provide early indicators of changing borrower conditions and emerging portfolio trends. As AI helps interpret those signals, the intelligence can support broader risk management, lending strategy, finance, and executive planning.

As financial institutions plan for 2027, data and AI investments will increasingly be judged by measurable business impact. Collections stands out because it combines rich behavioral data, portfolio performance, and operational execution in a single function.

The opportunity goes beyond automation. By combining behavioral data, predictive analytics, institutional knowledge, and AI-assisted guidance, financial institutions can identify risk earlier, prioritize resources more effectively, and make better decisions across the collections lifecycle.

For institutions evaluating where AI can deliver meaningful results, collections offers one of the clearest business cases. It generates the data, produces measurable outcomes, and creates opportunities to improve both operational performance and portfolio results.

That makes collections one of the strongest data and AI investments to include in a 2027 technology budget.

Why should banks and credit unions include collections technology in their 2027 budgets?

Collections is one of the most measurable areas for data and AI investment. Modern collections platforms can help financial institutions improve collector productivity, identify risk earlier, support digital engagement, and make more informed decisions using portfolio and behavioral data. These improvements can have a direct impact on operational efficiency and portfolio performance.

How can AI improve collections operations?

AI can help collectors quickly understand account history, surface relevant information, identify patterns, and recommend next actions. Instead of spending time manually reviewing multiple systems and notes, collectors can focus on borrower engagement, risk management, and resolution activities.

What data is most valuable for AI-enabled collections?

Effective collections intelligence often combines portfolio data, payment behavior, delinquency trends, communication history, promises-to-pay, recoveries, benchmarking data, industry insights, and predictive analytics. Together, these data sources help institutions better understand risk and prioritize resources.

How do predictive models help collections teams?

Predictive models help banks and credit unions move beyond traditional delinquency buckets by identifying which accounts are most likely to become severely delinquent and which are likely to self-cure. This allows teams to intervene earlier, focus resources more effectively, and create more targeted collection strategies.

What are the benefits of AI for credit unions and community banks with limited staff?

Many financial institutions face growing workloads without corresponding staffing increases. AI can help expand capacity by reducing time spent on account research, surfacing important information faster, and making institutional knowledge easier to access. This allows collectors to handle more accounts while maintaining consistency and compliance.

How can financial institutions measure the ROI of AI in collections?

Collections leaders can track metrics such as account research time, collector productivity, consistency in policy application, risk identification, queue prioritization, delinquency outcomes, and overall portfolio performance. Because collections is highly operational and outcome-driven, AI results are often easier to quantify than in many other business functions.