Key Risk Indicators are powerful metrics, but they are harder to understand than KPIs. Should you use KPIs or KRIs to measure your risk team’s success?
The short answer is that you need both. But because there still seems to be confusion between the two types of metrics and their usage, we wanted to break them down in an in-depth post.
Table of contents
- What Are Key Risk Indicators (KRIs)?
- KRIs Vs KPIs – What Are the Differences?
- How to Develop Key Risk Indicators
- Using KRIs to Calculate KPIs (and Vice Versa)
- How to Share Your KRIs?
- KRIs & KPIs: Taking a Holistic Approach to Fraud
- 6 Examples of Useful Fraud KPIs
- Infographic: Fraud Management KPIs Cheat Sheet
- Key Takeaways – Looking at Risk Beyond Fraud Rates
So without further ado, let’s start by looking at what Key Risk Indicators are, and how they can help your RiskOps, or risk operations.
What Are Key Risk Indicators (KRIs)?
A Key Risk Indicator, or KRI, is a measure that indicates how damaging an activity might be. It’s a key feature of RiskOps analysis, whose goal is to predict how likely an action is to hurt the company, either financially or because of a bad reputation.
This is especially useful for upcoming projects, whether it’s to take on more transactions, attend a public event, or launch 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. Key Risk Indicators, on the other hand, point to future adverse impact.
In other words, KRIs can be used to measure risk that hasn’t happened yet, which is useful for unveiling new growth opportunities, or assessing which processes need to be optimised.
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 mesure 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, it’s up to you to decide how transparent you are with them. They can be useful to bring up to upper management, especially if you foresee a drastic increase in risk.
If they touch upon more personal performance (for instance those looking at cost saved per agent), you might have to mask the agent’s identity by using IDs. Too much open data may make people uneasy and actually disincentivise the worst performing team members.
However, sharing enough data with the team may help them self-regulate and help each other without requiring additional push from management.
You can also use KRIs to justify promotions, bonuses, or internal training if needed. It’s also useful to know if you should invest in specific software or improved infrastructure, as you can measure ROI against concrete numbers (hours saved, P&L, employee performance, etc…).
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 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 prevention solution, 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
Key Takeaways – Looking at Risk Beyond Fraud Rates
The key question many businesses fail to answer is: how do you measure something that isn’t there?
More specifically, how can you fight fraud if you’re not even certain that you are a target? Let’s not forget that the ultimate goal of fraudsters is to not get caught. If they are successful, you’ll have an excellent fraud rate – but that won’t mean you’re not under attack. Conversely, if your fraud prevention system is too rigid and treats every customer as a fraudster, you’ll lose out on business.
The solution is to take a holistic approach to risk management and RiskOps, and to measure potential threats as well as existing ones. This is precisely where KRIs or Key Risk Indicators can help.
You might also be interested in reading about:
- SEON: Best Fraud Detection Software
- SEON: Guide to Ecommerce Fraud Detection & Prevention
- SEON: Fraud Detection & Prevention – How to Find the Right Tools And Solutions
- SEON: Using an IP Fraud Score to Detect High-Risk Users
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