Effective Risk Management: KPIs Vs KRIs (Key Risk Indicators)

Key Risk Indicators are powerful metrics but are harder to understand than KPIs. Is it better to use KPIs or KRIs to measure your risk team’s success?

The short answer is that you need both. However, because there still seems to be confusion between the two types of metrics and their usage, we wanted to break them down so you understand these concepts better. Let’s start by looking at Key Risk Indicators and how they can help your risk operations.

What Are Key Risk Indicators (KRIs)?

A Key Risk Indicator (KRI) indicates how damaging an activity might be. It’s a key feature of RiskOps analysis and risk monitoring, which aim to predict how likely an action will hurt the company financially or through a bad reputation.

This is especially useful for upcoming projects, such as taking on more transactions, attending a public event, or launching a new product.

KRIs Vs KPIs – What Are the Differences?

The key difference between a risk management KPI and a KRI is that key performance indicators are designed to measure how well (or badly) things are going using historical data. On the other hand, Key Risk Indicators point to future adverse impacts.

In other words, KRIs can measure risk that has yet to occur, which is useful for unveiling new growth opportunities or assessing which processes need to be optimized.

An example of KPI

A good way to know if you’re dealing with a KPI is to ask if it measure how well your risk team doing. A KPI example would be to log and monitor the chargeback dispute success rate per agent.

An example of KRI

If the data you use measure helps anticipate a risk factor, then it’s a KRI. An example would be to estimate how many more cybersecurity attacks you would risk by launching a new product.

How to Develop Key Risk Indicators

As mentioned above, KRIs help us see where risk could potentially exist. Using them can help with a multitude of scenarios when working with unknowns:

Key Characteristics of a KRI

A KRI should help you answer questions in the following scenarios:

• Anticipating new risk factors,
• Justifying additional headcount,
• Identifying risk that doesn’t yet damage the company,
• Setting up defences before new risk vectors arise,
• Organising team roles in anticipation of new risks,

Selecting and Tracking a KRI

As you can imagine, there are two things you need to deploy them: a strategy and access to the right data. The strategy part has to do with the selection of the right KRI, but also understanding if you will be able to measure it.

Access to the right data comes from your tools. Is your data accurate? Can you quantify doubts with numbers? And which monitoring, tracking and reporting tools do you use?

Using KRIs to Calculate KPIs (and Vice Versa)

An interesting point is that you can actually use these large-scale metrics to focus on more granular KPIs. Following our key characteristics of KRI, you could use the cost of an attack against your business to:

• Monitor the team’s performance per shift,
• Look at individual agent’s performance,
• Measure the cost of blocking certain user actions,
• Measure the time lost to working on a specific task

Interesting (and very useful questions you could answer include):

• How much cost did you save by focusing on a specific task?
• How expensive was it to miss actions that damaged your bottom line?
• How much do you save when an agent meets a goal?

And crucially, you could estimate the value of hidden or invisible risk.

How to Share Your KRIs?

Once you have found a satisfactory way to calculate the metrics, you must decide how transparent you will be with them. Sharing these metrics with upper management can be beneficial, especially if there is a significant increase in risk. However, if the metrics touch upon personal performance, such as cost saved per agent, consider masking identities to avoid making employees uneasy and disincentivizing the worst performers.

Sharing enough data with the team can foster self-regulation and mutual support without management intervention. Additionally, Key Risk Indicators (KRIs) can justify promotions, bonuses, or internal training. They are also valuable for deciding on investments in software or infrastructure by providing concrete ROI measurements, such as hours saved and improvements in employee performance.

KRIs & KPIs: Taking a Holistic Approach to Fraud

Now we’ve established the difference between KPIs and KRIs, let’s see how this applies to fraud prevention.

In the context of risk management, KRIs help managers with their balancing act. On the one hand, they want to block as many fraudulent transactions as possible. On the other, they want to accept as many transactions as possible. If you were to block all transactions, the fraud rates would drop down to 0%.

So a standard KPI for measuring fraud rates would look like:

Fraud = chargebacks + refunds / total accepted transactions in a given time period.

But these results also need to weigh in your acceptance rate (ratio of approved vs declined transactions).

Moreover, factoring false positives into the equation can be tricky, because you may lose more than the value of a transaction. A false positive could turn a loyal customer towards competitors, which means your customer lifetime value (CLV) and customer acquisition costs (CAC) are also wasted.

So we’re now already looking at a much more complex equation:

Cost of fraud = transaction value + chargeback fees vs. false decline = transaction value + CLV + CAC

As you can see, the cost of fraud can be a very strong KRI because it gives us a better view of how fraud affects various business areas. 

More importantly, you can use that number to spot when something goes wrong. If, say, a payment service goes down, you should immediately see a change in the numbers.

6 Examples of Useful Fraud KPIs

For this example, we’ll look at KPIs that are relevant for online stores and e-commerce, but many of them can be adapted to any kind of transaction, be it a SaaS or a B2B business.

• Original Approval Rates: Before looking at how to reduce fraud, you need to check what percentage of your transactions are approved. There are various schools of thought on how to best calculate that number, but it’s important to take into account things such as whether auto declines come from the issuer bank, payment gateway or your own fraud prevention system. 
• Chargeback Rates: Chargeback disputes & chargeback fraud are the bane of any fraud manager’s existence. Should the rates trend above the dreaded 1% rate, an online store might even be labelled high risk, and lose a partnership with the card network. The challenge is that chargeback rates tend to be calculated differently depending on the credit card processor. It’s important to take these differences into account when calculating your own rates, and you can even break them down further by looking at individual payment methods (for instance PayPal vs WePay).
• Average Manual Review Time Per Agent: A self-explanatory metric, but one that can be extremely useful to justify a promotion, assign various workloads, or to initiate a performance review. 
• Checkout Abandonment Rates: A useful KPI to share with marketing, as they may use the numbers to test out automatic email campaigns. For fraud and payment, it’s useful to look at how much friction your payment gateways and prevention tools add to the customer journey. For instance, if the checkout abandonment rates boom after implementing extra prevention checks, you could look at dynamic friction solutions (when extra KYC checks are only initiated after risk scores go above a set threshold).
• Cost Per Analysis: One of the best ways to calculate ROI on your fraud detection platform, and to understand the full cost of fraud at your company. You should include all the expenses related to manual and automatic review, lost customer lifetime values from declined orders, and how much is saved when a fraud attempt is caught by the system.  This is especially useful when you work with a pay-per-API call fraud prevention engine. As we’ve previously covered, a chargeback-guarantee model may offer better value on paper, but also a strong incentive to be conservative when taking risks, resulting in a higher rate of false positives (which would impact your overall revenue in the long run).
• Fraud to Sales Ratio: A simple metric that is useful both as a KPI and risk indicator. If the number rises too steeply, you know you’ll need to consider other fraud prevention solutions or strategies.

Infographic: Fraud Management KPIs Cheat Sheet

How SEON Can Help

SEON provides a robust, customizable, and fully explainable fraud scoring system that enhances risk assessment for each transaction through a combination of human intelligence and machine learning. Here’s how SEON helps with risk assessment and provides a risk score based on the following components:

• Default rules: SEON’s platform includes best-practice default rules created by in-house fraud analysts. These rules can be easily toggled on or off, and are grouped into convenient categories. 
• Custom rules: Users can create custom rules tailored to their specific needs, controlling the risk scoring down to the decimal point. Custom rules can automatically approve, reject, or send transactions based on predefined criteria for manual review.
• Machine learning rules: SEON’s algorithms generate explainable rule suggestions based on past activity. These suggestions come with confidence scores, allowing users to understand and modify them before implementation. Users can also automate the application of machine learning rules that meet a certain confidence threshold for a more hands-off approach.
• Industry presets: SEON offers industry-specific rulesets based on observed fraud trends and patterns, catering to sectors like fintech, iGaming, BNPL, online lending, and travel.These presets provide targeted fraud prevention tailored to each industry’s unique challenges.
• Blackbox fraud scoring: SEON also includes a blackbox machine learning module that independently calculates the likelihood of fraud for each transaction. This score operates alongside the whitebox scores and can identify new fraud patterns that predefined rules and the human eye may not catch.

By offering a comprehensive and flexible fraud scoring system, SEON enhances risk assessment capabilities, providing more accurate, real-time evaluations and helping to safeguard against fraud effectively.

KPIs Vs KRIs FAQ

You might also be interested in reading about:

• SEON: Fraud Detection & Prevention: What is it and How to Find the Right Features
• SEON: Using an IP Fraud Score to Detect High-Risk Users
• SEON: Fraud Risk Management: How to Implement It & Advanced Technologies

Learn more about:

Browser Fingerprinting | Device Fingerprinting | Fraud Detection API | Fraud Detection Using Machine Learning

External Sources:

• Ecommerce Nation: How to Calculate Cart Abandonment Rate
• Neil Patel: How to Calculate Lifetime Value