You already know which actions on your platform carry more risk and the kind of verification they require. The next challenge is making sure your system recognises those moments as they happen and requests the right check before the transaction or action goes through.

Step-up authentication involves more than asking users for another OTP or biometric check. It depends on three connected parts: identifying risk signals, deciding when those signals warrant additional verification, and carrying out the appropriate check. If these parts don't work together, users may face unnecessary challenges while genuinely risky sessions slip through.

This guide breaks down how to build each part, connect the decision-making process to the verification flow, test the implementation before launch and monitor how well it performs in production.

Component 1: The Risk Signal Layer

The risk signal layer identifies activity that may carry more risk than usual and passes that information to the decision engine.

Start with three things when building the risk signal layer:

• Build a behavioural baseline for each account: Compare activity against the account’s own history, not just population averages. Transaction amounts, timing, device consistency and beneficiary patterns help establish what is normal for each user. Without that baseline, the system may struggle to distinguish unusual activity from a user’s regular behaviour.
• Define the signals that contribute to the risk score: Common signals include device changes, new beneficiaries, unusual transaction amounts, account inactivity and location changes. A new device may pose little risk alone but warrant closer scrutiny alongside a recent SIM swap and a high-value transfer to a new beneficiary. Risk and compliance teams should agree on how each signal affects the score before implementation.
• Architect specifically for SIM-swap signals in African markets: SIM swaps can expose OTP-based authentication when attackers take control of a user’s registered phone number. Where available, use SIM-change signals  to identify changes to the SIM or IMSI associated with the account. Treat a recent SIM swap before a high-value action as a strong risk signal.

With the signal layer producing a score, the decision engine maps that score to a response.

Component 2: The Decision Engine

The decision engine takes the risk score from the signal layer and determines what happens next.

Build the decision engine around these requirements:

• Define three response tiers: Allow low-risk actions to proceed, require an OTP or PIN for medium-risk activity, and trigger biometric verification, liveness detection or a face match for high-risk actions. This risk-based authentication approach maps risk scores to allow, challenge or block decisions. Calibrate thresholds for each tier during the pilot, using real transaction patterns rather than arbitrary values.
• Make decision rules configurable: Hardcoded thresholds require an engineering deployment to change, while configurable rules let product or compliance teams adjust them through a dashboard. This allows the team to respond to changing user behaviour and fraud patterns without a code release.
• Define the fallback for failed verification: Offer a guided retry, an alternative verification method or a transaction hold for manual review when a user fails a liveness check. Without a fallback, the system may block legitimate users from completing transactions and increase support requests. Define these paths before go-live.

With the decision engine routing to the right response, the verification layer needs to execute that response fast enough that legitimate users are not disrupted.

Component 3: The Verification Layer

The verification layer is the part of step-up authentication the user interacts with when an additional check is required.

Choose the right verification method for each risk level:

• Match the verification method to the risk level: Use an OTP or PIN for medium-risk activity and biometric verification or liveness checks for higher-risk actions. Where the action requires stronger assurance, combine liveness detection with face matching. Avoid applying the most demanding check to every transaction.
• Account for mobile conditions: Test the verification flow on lower-end Android devices and slower network connections common across African markets. Long loading times, failed camera access and dropped sessions can prevent legitimate users from completing a check. Keep the flow lightweight and provide clear instructions when a step fails.
• Verify before completing the action: Enforce the verification result on the backend before executing the transaction or sensitive account change. A frontend-only check can be bypassed if the backend accepts the action without confirming the result. Ensure the backend rejects or holds the action until verification succeeds.

With all three components built, the implementation needs to be tested before real users hit it.

Testing Before Go-Live

The most common step-up authentication failures come from miscalibrated thresholds that trigger too often or fail to catch high-risk activity.

Before enabling step-up across the full user base, focus on three checks:

• Run a pilot to calibrate thresholds: Apply risk scoring to a subset of accounts without triggering step-up verification, then review the sessions that would have triggered a challenge. Use the results to distinguish genuine risks from false positives and adjust thresholds before launch.
• Test every failure scenario: Test liveness check failures without a fallback, timeouts during verification and scores that land exactly on threshold boundaries. Define and test the handling path for each scenario before real users encounter it.
• Verify the audit trail: Ensure every step-up decision records the triggering signals, risk score, requested verification, outcome and resulting action. These records provide evidence of implemented controls and support reviews against applicable authentication and AML/CFT requirements.

After go-live, the same signals that feed the risk scoring layer are the signals to monitor for integration health.

Monitor After Launch

After launch, monitor whether step-up authentication catches risky activity without creating unnecessary friction for legitimate users.

Track three metrics to assess performance:

• Step-up trigger rate: Measure how often the system requests additional verification. A high rate may indicate thresholds that are too sensitive, while a low rate may mean the system is missing risky activity.
• Verification completion rate: Track how many users successfully complete the requested check. A low rate may point to friction in the verification flow, technical issues or difficulties using the method on certain devices.
• False-positive rate: Review how often legitimate sessions trigger step-up verification. Analyse the signals behind these decisions to identify rules that challenge users unnecessarily and adjust them accordingly.

Together, these metrics show whether step-up authentication is protecting the platform or creating friction for legitimate users without catching enough fraud.

How Dojah Supports Step-Up Authentication Implementation

Implementing step-up authentication requires risk scoring, verification methods and decision rules that connect them. Dojah offers tools that can support these requirements through risk monitoring and biometric verification.

Here’s how Dojah’s tools support each part of the implementation:

• Risk scoring with Profiled Risk: Profiled Risk analyses behavioural, device and transaction signals to produce risk scores. These scores can inform rules for identifying activity that requires additional verification.
• Liveness checks for additional assurance: Dojah’s Liveness Check helps verify that a real person is present during a verification attempt. It can support step-up flows when additional assurance is needed.
• Face matching for identity verification: Dojah’s Biometric Verification compares facial biometrics against an identity document. This can support stronger identity checks when a risk decision calls for additional verification.

For African fintechs implementing step-up authentication, Dojah’s risk scoring and biometric verification tools can support the process of identifying risky activity and verifying users.

Explore how Dojah supports step-up authentication for African fintechs.

Frequently Asked Questions About How to Set Up Step-Up Authentication for High-Risk Moments on Your Platform

1. What is step-up authentication, and how does it work?

Step-up authentication requests an additional identity check when an activity presents elevated risk. Depending on the risk level, users may need to complete an OTP, PIN or biometric check before the action proceeds.

2. When should a fintech trigger step-up authentication?

Trigger it during high-value transfers, changes to sensitive account details, new beneficiary additions or logins from unfamiliar devices. Combining these events with other risk signals helps identify when additional verification is necessary.

3. How can fintechs implement step-up authentication in Africa without disrupting users?

Fintechs should define risk thresholds, match verification methods to the level of risk and test the flow before launch. They should also account for mobile device limitations and network conditions to reduce unnecessary friction.

4. How does risk-based authentication in Africa help prevent fraud?

Risk-based authentication evaluates signals such as device changes, unusual transaction patterns and unfamiliar login locations. Fintechs can use these signals to allow routine activity, request additional verification or block high-risk actions.

5. What is the difference between step-up authentication and multi-factor authentication (MFA)?

MFA uses multiple authentication factors to verify identity, while step-up authentication determines when additional verification is needed. Step-up authentication can use MFA to strengthen security when a session or transaction presents elevated risk.

6. How can fintechs integrate a step-up verification API in Nigeria?

Fintechs can connect risk assessment and verification services through their backend, using predefined rules to trigger additional checks. The backend should validate the verification result before completing the action and log each decision for monitoring and review.