Should You Build Your Own Branch Risk Scoring Model?

For security directors weighing the build-vs-buy decision for a branch risk-scoring model, the math rarely works out the way leadership hopes

There’s a particular moment many security directors will recognize. In a senior team meeting, someone looks at the line item for a third-party branch risk intelligence provider and asks the question: “Why can’t we just build this ourselves?”

It may seem a reasonable question on the surface. Your team is talented. You have data. You have analysts who understand your network. And at first glance, a risk scoring model sounds like something that could be developed and updated as needed. But it rarely works out that way.

This article isn’t meant to discourage innovation or internal capability-building broadly. There are plenty of things financial institutions should build in-house. Branch risk scoring, for most organizations, is not one of them. Here’s why.

The Complexity Hiding Behind “Just Build a Model”

A branch risk score sounds simple enough…just assign a number to a location that reflects its risk for robbery, fraud, or other criminal activity. But what information should go into creating a defensible, actionable score? This question reveals layers of complexity that aren’t obvious until you’re deep into the project.

What data do you actually need?

Credible branch risk scoring draws on a wide range of inputs: historical crime data (which varies enormously in quality and granularity by jurisdiction), internal incident reports, reports from peer institutions, demographic and socioeconomic indicators, proximity to known high-risk corridors, time-of-day and seasonal patterns, findings from internal risk assessments, and physical security attributes of the branch itself. Each of these data sources require time and review on its own, a task that can eat up a lot of your team’s time.

Public crime data, for instance, is notoriously inconsistent. Reporting methodologies differ by city and county. Data is delayed, sometimes by months. Some agencies won’t share the data. Some charge (and sometimes a significant amount) for the data. Some jurisdictions have moved away from granular public reporting altogether. And there’s no consistency required in how this information is recorded or shared. Building a model that relies on these data can be costly, time-consuming, and due to the inherent limitations, unreliable.

Who builds it, and what do they actually know?

Developing a valid risk model requires more than analytical skill. It requires domain expertise in physical security, loss prevention, criminology, and financial crime. Because when you are using risk models to develop safety and security protocols, accuracy is everything, and close enough isn’t close enough.

The Hidden Costs That Don’t Show Up in the Initial Budget

When stakeholders ask about building internally, the conversation usually centers on development costs. But creating the model is just the beginning. The ongoing total cost of data acquisition, validation, and management; model calibration and risk exposure. This is why in-house models consistently underperform the business case.

What follows is a deeper dive into some of these cost factors that contribute to an escalating and sometimes unpredictable total cost of ownership for a risk scoring model.

• • Data acquisition cost and management:  The data inputs required to create an accurate branch risk model are not always free. Some agencies offer their crime reports for free, but not all. And crime data feeds, demographic databases, geospatial services, and peer institution benchmarking data come with acquisition and/or licensing costs. Add the infrastructure to store, process, and version-control that data and the time to maintain those information pipelines, and you’re looking at a cost base that frequently exceeds what a third-party provider charges, before you’ve written a single line of model code.
  • Model validation and ongoing calibration: A model that isn’t regularly validated against outcomes is a model that drifts. Branch risk environments change over time: neighborhoods evolve, criminal patterns shift, and new threats emerge. Keeping a scoring model correctly calibrated requires a continuous feedback loop between model outputs and observed incidents, along with the analytical resources to interpret what the data are telling you and adjust accordingly. This is not a one-time capital  cost. It’s an ongoing operational expense that tends to be underestimated in initial proposals and can be deprioritized when other priorities compete for the same team’s attention or dollars.
  • Regulatory and audit risk exposure: When examiners or auditors ask how your institution assesses and manages branch-level risk, “we built a model” isn’t the best answer. You need documented methodology, validation records, evidence of ongoing calibration, a clear chain of accountability, and proven objectivity. For an internally developed model, all of that documentation burden falls on your team, and gaps in it can create examination findings that are more costly than the tool they were meant to replace.

The Opportunity Cost You May Not Think Of

Every hour your security analysts spend building, maintaining, and defending an internal risk model is an hour they’re not spending on other crucial work: threat intelligence, incident response, physical security program development, cross-functional collaboration with fraud and operations, and more.

Security directors are already stretched for time. The talent on these teams can be hard to hire and easy to lose. Directing that talent toward model maintenance can be a costly misallocation. Businesses want security departments to focus on the business and delegate the responsibility of security it to security experts. Security departments often buy these risk scoring tools for the same reason…they want to focus on broader security issues and delegate responsibilities around the risk tool and data to risk model experts.

Internal development can also create false confidence. An internally built model that produces authoritative-looking outputs can increase organizational risk if decision-makers treat those scores as validated when they aren’t. A number that looks precise is not the same as a number that’s right.

What Specialized Providers Actually Deliver

The case for third-party branch risk scoring isn’t simply about convenience. It’s about access to something that most institutions genuinely cannot replicate at reasonable cost: scale, specialization, and continuous improvement built on aggregated experience across the industry.

Purpose-built providers operate at a scale that makes investments in data acquisition, model development, validation infrastructure, and criminological expertise reasonable. A single institution couldn’t replicate that model. With teams whose entire focus is on the accuracy and utility of this one problem, dedicated providers incorporate feedback from dozens or hundreds of client deployments and absorb the regulatory and validation burden as part of the product.

When the question is “can we build this ourselves,” the honest answer is usually: technically, maybe. But “will what we build be better than what we can buy, at a lower total cost, without distracting us from our security responsibilities, and with less risk to our people and our program?” is invariably: no.

The Right Role for Internal Expertise

None of this means your security team should be passive consumers of vendor-produced scores. The most effective programs combine third-party scoring with internal expertise, and sometimes internal event data, using externally validated risk signals as a foundation while layering in institution-specific context, operational judgment, and local knowledge that no provider can fully replicate.

That’s a very different model than building a scoring methodology from scratch. And it’s a much better use of your team’s time. The goal isn’t to outsource judgment. It’s to make sure the analytical foundation your judgment rests on is as solid as it can be and that your people are focused on the work that actually requires their expertise.