Integrating a trust scoring API is a two-part problem. The first part is the technical integration: getting the SDK installed, the API key configured, and the score call returning a value. That part is measured in hours for most platforms. The second part is the harder one: deciding where in your transaction flow the score call belongs, what threshold triggers which response, and how to handle the latency budget.
This guide covers the second part. Most of the questions we hear from engineering teams evaluating Karma3 are about placement strategy, not implementation mechanics.
The three score placement moments
There are three natural friction points where a behavioral trust score adds the most coverage: at authentication, at checkout, and at payout. Each surface has a different risk profile and a different latency budget.
At authentication, you are scoring the session that just started. The behavioral signals available are pre-login navigation, input cadence on the credential form, and device fingerprint consistency against account history. The score latency budget is generous because the user is already waiting for the authentication response. Adding 60-80ms for a score call is invisible to the user experience.
At checkout, the session is warmer. More behavioral context has accumulated: browse history, item selection patterns, address field interactions. The risk question is different from authentication. You are no longer asking whether this is the account owner; you are asking whether this session's behavior is consistent with a legitimate purchase intent. The score at checkout synthesizes the full session behavioral trace, not just the authentication moment.
At payout, the stakes are highest and the latency budget is most constrained. For platforms processing payouts synchronously, the score call must complete within the payout request processing window. Karma3's median score latency is 67ms. For asynchronous payout queues, the score can be computed ahead of payout execution and cached against the payout request ID.
Score placement in a synchronous auth flow
For a synchronous authentication flow, the typical integration pattern is a server-side score call after credential verification, before session token issuance. The flow looks like this:
POST /auth/login
1. Validate credentials (existing auth layer)
2. If credentials valid:
a. Call Karma3 /score with session_id + behavioral payload
b. Receive trust_score (0.0 - 1.0)
3. Apply threshold policy:
- trust_score >= 0.7: issue session token (pass)
- trust_score 0.4 - 0.69: step-up to MFA
- trust_score < 0.4: hard block + log
4. Return auth responseThe behavioral payload for the score call is assembled by the client-side SDK and transmitted as an encrypted blob with the credential submission. Your server decrypts the payload and passes it to the Karma3 API along with the session ID. The score call is a single POST with a JSON body; the response returns in under 100ms in the vast majority of cases.
Handling the latency budget
For platforms with strict authentication response time SLAs, the score call can be made asynchronously after an initial authentication grant with a provisional session token. The provisional session has restricted permissions. On score response, the session is upgraded to full permissions or downgraded to step-up flow.
This pattern works well for platforms where the post-login landing page has its own API calls that run in parallel with the score request. The score completes before the user can take a high-risk action, while the login response itself is immediate.
Thresholds and your policy layer
Karma3 returns a floating-point trust score. The threshold logic that determines what response to take is yours to configure. We do not prescribe a specific threshold structure, because the right thresholds depend on your platform's fraud tolerance, your MFA step-up cost, and the distribution of your user base's behavioral diversity.
What we can share from early-access deployments: platforms that start with a single hard-block threshold at a low score (below 0.2) and a step-up threshold in the middle range (0.4-0.5) tend to see the best balance of fraud reduction and user friction reduction in the first 30 days. From there, you tune based on the score distribution you observe in production.
SDK vs. REST API integration
The SDK collects the behavioral signal on the client and encrypts it before transmission. If you have a native mobile app and a web platform, the SDK is the fastest path to full behavioral coverage because it instruments device fingerprinting and input cadence automatically.
If you have an existing behavioral data collection layer or a server-rendered application where client-side instrumentation is limited, the REST API accepts behavioral event payloads that you construct from your own event data. The payload schema is documented and covers the same signal families the SDK collects. The score quality from a well-structured custom payload is equivalent to the SDK path.
