The Marketplace Credit Risk and Fraud Analyst

Description

What is the The Marketplace Credit Risk and Fraud course about?

Build the analyst skillset that turns chargeback signals, BNPL exposure, and merchant credit reviews into one defensible decision file. The fraud model says decline. The merchant says they have been selling on the platform for three years. The finance partner says the chargeback rate is fine. The risk lead wants a recommendation by Thursday. Whose number wins? Includes a hand-built implementation playbook.

Why this course?

Credit risk and fraud analysts inside large commerce platforms sit at an awkward junction. The fraud team owns the rules engine. The credit team owns the merchant cash advance and BNPL exposure. The disputes team owns the chargeback queue. The merchant success team owns the relationship. Every threshold change touches all four, and the analyst is usually the person who has to.

What do you take away from the The Marketplace Credit Risk and Fraud course?

Build a chargeback-to-approval-rate curve that a finance partner accepts as the basis for threshold decisions. Model BNPL and merchant cash advance exposure against merchant tenure, vertical, and chargeback history in one scorecard. Read first-party fraud, bust-out, and synthetic identity patterns at signup using device, behavioural, and document signals together. Write the one-page decision memo that closes the threshold or underwriting argument across.

What you get with this course?

Twelve written modules in the Art of Service learning environment, each anchored to a concrete analyst situation. Downloadable templates for the decision memo, the chargeback-to-approval curve, the exposure scorecard, the rule change A/B framework, and the quarterly portfolio review memo. Worked example for each module showing the artefact filled in for a fictional marketplace portfolio with realistic vertical mix and exposure shape.

What you will have in hand by Day 1, Week 1, Month 1?

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it. Modules 1 through 4 are designed to be worked through in the first week, producing a draft chargeback curve and a draft exposure scorecard for your own portfolio. Modules 5 through 8 build out the onboarding signal pattern and the rules and.

What does the The Marketplace Credit Risk and Fraud cover on before and after?

The fraud model output gets forwarded into a thread, finance and merchant success argue past each other on revenue versus false positives, the threshold change gets deferred for another cycle, and the analyst has produced data without producing a decision. The one-page decision memo lands in the review channel with the chargeback curve, the exposure number, the false-positive estimate, and the recommended.

What happens if you do not address this?

The card network monitoring notice arrives, the BNPL portfolio takes a loss the quarterly review did not flag, or the threshold gets set by whoever argued loudest rather than by whoever brought the evidence. The analyst seat starts to feel like a queue rather than a discipline, and the senior analyst opening goes to someone whose memos closed arguments.

Who it is for?

Credit risk or fraud analyst inside a marketplace, payments platform, or large e-commerce platform with first-party seller financing, BNPL, or merchant cash advance exposure. Comfortable in SQL, familiar with the rules engine and the model output, but tired of being the person whose recommendation gets argued down because the memo did not connect the fraud signal to the credit exposure and the.

More answers: what you get with every course, refund policy, all help answers.

A focused course, tailored for you

The Marketplace Credit Risk and Fraud Analyst Playbook

Build the analyst skillset that turns chargeback signals, BNPL exposure, and merchant credit reviews into one defensible decision file.

The fraud model says decline. The merchant says they have been selling on the platform for three years. The finance partner says the chargeback rate is fine. The risk lead wants a recommendation by Thursday. Whose number wins?

$199 one-time

Tailored to your situation. Access within 24 hours. 30-day money-back.

Includes a hand-built implementation playbook delivered alongside course access, generated for your specific situation.

Why this course

The specific moments where this gets uncomfortable: a bust-out cluster appears in a vertical and the rules engine catches half of it, the BNPL portfolio takes a hit and finance asks whether the underwriting criteria should tighten, a card network sends a chargeback monitoring notice and disputes asks whether the model is calibrated, or a merchant cohort with three years of clean history suddenly trips the velocity rule.

In each of these, the analyst who can produce a one-page decision file with the chargeback curve, the exposure model, the false-positive estimate, and the recommended threshold change in plain English ends the conversation. The analyst who can only forward the model output gets overruled by whoever has the louder partner. This course teaches the file.

What you walk away with

Build a chargeback-to-approval-rate curve that a finance partner accepts as the basis for threshold decisions.
Model BNPL and merchant cash advance exposure against merchant tenure, vertical, and chargeback history in one scorecard.
Read first-party fraud, bust-out, and synthetic identity patterns at signup using device, behavioural, and document signals together.
Write the one-page decision memo that closes the threshold or underwriting argument across fraud, credit, disputes, and finance.
Defend the analyst recommendation in the cross-functional review with the evidence file ready before the meeting starts.

The 12 modules

Module 1. The Analyst Decision File

Start with the artefact this whole course builds toward: the one-page decision memo with the recommendation, the chargeback curve, the exposure number, and the false-positive estimate. The module shows three live examples of memos that ended the threshold argument and three that did not, and the difference between them. By the end you have the template you will fill in across the remaining eleven modules.

Module 2. Chargeback-to-Approval Curve as the Core Chart

How to build the chart that finance and risk both accept. Walks through pulling the right disputes data, segmenting by reason code that actually maps to fraud versus merchant credit versus quality, choosing the time window that smooths noise without hiding regime change, and overlaying the approval rate so the marginal economics of every threshold change are visible. Includes the SQL pattern and the chart template.

Module 3. BNPL and Merchant Cash Advance Exposure Modelling

The credit side of the analyst's job. Walks through the exposure ledger, how to model expected loss against merchant tenure, vertical, and historical chargeback rate, how to flag concentration in a single vertical or geography, and how to translate the model into the underwriting threshold conversation. Includes a scorecard template you can adapt to the portfolio mix you cover.

Module 4. First-Party Fraud and Bust-Out Patterns at Signup

Reading the onboarding signals. Device fingerprint, IP and geolocation, document quality, behavioural session signals, and credit bureau pull together rather than each in isolation. The module names the specific pattern combinations that flag bust-out before the first chargeback hits, and the specific combinations that look like fraud but are actually a legitimate merchant with bad onboarding hygiene.

Module 5. Synthetic Identity at Merchant Onboarding

Synthetic identity in marketplace onboarding looks different from consumer card synthetic. The legal entity, the controlling person, the bank account, and the tax ID can each be real while the combination is fabricated. The module covers the specific cross-checks that catch the synthetic combination, the signals from bank account verification and tax ID verification that matter, and how to write the case for declining without exposing the platform to a discrimination complaint.

Module 6. Card Network Monitoring Programmes and What Triggers Them

Visa VDMP, Mastercard ECP, and the equivalent programmes set hard thresholds on chargeback rate and fraud rate. Crossing them costs the platform real money and can lose acquiring relationships. The module covers what the thresholds actually are, how the rolling-window calculation works, how to read the early-warning signals before a notice arrives, and how to write the remediation memo when one does.

Module 7. Rules Engine Calibration the Analyst Side

You are not going to rebuild the rules engine, but you are going to argue for the next threshold change. The module covers how to read the rule-level performance report, how to estimate the false-positive impact of a proposed change on genuine merchants, how to A/B test a rule change in shadow mode before it goes live, and how to present the result so the rules engineering team treats your recommendation as evidence, not opinion.

Module 8. Model Calibration Without Owning the Model

The fraud model produces a score. Sometimes the score is well calibrated, sometimes it has drifted, sometimes a population segment was underrepresented in training. The analyst's job is to spot when the model is wrong and write the case to the data science team. The module covers calibration plots, segment-level performance, and the specific pattern of evidence that gets a data science team to actually retrain rather than push back.

Module 9. Cross-Functional Review Choreography

The weekly or biweekly cross-functional review is where threshold decisions actually get made. The module covers how to seed the agenda with the right data ahead of the meeting, how to handle the predictable pushback from finance on revenue impact and from merchant success on false positives, how to use the chargeback-to-approval curve to anchor the conversation, and how to leave the meeting with a decision recorded rather than a follow-up scheduled.

Module 10. Writing the Decline Reason That Survives Audit

Every decline has to be documented in a way that survives a regulator audit or a merchant complaint. The module covers what the decline reason has to include, what it must not include to avoid disparate impact exposure, how to log the evidence that supported the decision, and how to write the merchant-facing communication that does not invite a chargeback or a social media complaint.

Module 11. The Quarterly Portfolio Review Memo

Once a quarter the analyst writes the portfolio review. Loss rate by vertical, chargeback rate by cohort, BNPL exposure trend, top loss contributors, and the recommended underwriting and threshold changes for the next quarter. The module covers the structure that gets read all the way through, the charts that survive being copied into a board deck, and the recommendations framing that gets approved rather than deferred.

Module 12. Moving From Analyst to Risk Strategy

The capstone module. How the analyst transitions to senior analyst, manager, or risk strategy. What the next-level role owns that the current role does not, the work you can volunteer for now to demonstrate readiness, the conversations to have with your manager about the path, and the portfolio of artefacts that proves the move. Closes with the analyst as a candidate for the next seat.

How this addresses your situation

Specific modules that map to what you said you are dealing with.

The cross-functional review where the threshold change is being argued and the analyst has to walk in with the evidence file ready.

The BNPL or merchant cash advance portfolio meeting where finance asks whether underwriting needs to tighten and credit risk has to answer in numbers.

The card network monitoring notice that arrives and the disputes team asks whether the fraud model is calibrated and the analyst has to write the remediation memo.

The bust-out cluster that appears in a vertical, half-caught by the rules engine, and the analyst has to recommend whether to widen the rule, retrain the model, or tighten onboarding.

What you get with this course

Twelve written modules in the Art of Service learning environment, each anchored to a concrete analyst situation.
Downloadable templates for the decision memo, the chargeback-to-approval curve, the exposure scorecard, the rule change A/B framework, and the quarterly portfolio review memo.
Worked example for each module showing the artefact filled in for a fictional marketplace portfolio with realistic vertical mix and exposure shape.
Hand-built implementation playbook tailored to the portfolio mix the buyer describes at purchase, delivered alongside the course.

What you will have in hand by Day 1, Week 1, Month 1

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.

Modules 1 through 4 are designed to be worked through in the first week, producing a draft chargeback curve and a draft exposure scorecard for your own portfolio.

Modules 5 through 8 build out the onboarding signal pattern and the rules and model calibration arguments over week two.

Modules 9 through 12 close with the cross-functional choreography, the decline-reason audit defence, the quarterly memo, and the next-seat transition over week three.

Before and after

Before

After

The one-page decision memo lands in the review channel with the chargeback curve, the exposure number, the false-positive estimate, and the recommended threshold. The cross-functional review ends with a decision recorded against the analyst's recommendation.

What happens if you do not address this

Who it is for

Who this is NOT for. Not for fraud platform engineers who own the rules engine internals, not for chargeback dispute representatives who handle individual case workflow, not for credit underwriters at standalone lenders without a marketplace exposure, and not for fraud analysts at card issuers whose chargeback economics work differently.

How it arrives

Text-based course in the Art of Service learning environment, plus downloadable templates and worked examples for every module, plus the hand-built implementation playbook delivered alongside course access.

Time investment. Roughly two to three hours per module, twenty-five to thirty hours total across three weeks if worked through at a steady pace. The decision-memo template is usable in a live cross-functional review by the end of week one.

Why $199 is the right number

Generic fraud analytics courses teach the rules engine vendor's product and stop at the score. Generic credit risk courses teach consumer underwriting that does not transfer to a marketplace exposure. Big-four consulting decks are written for the head of risk, not the analyst, and assume the reader has staff. This playbook is written for the analyst seat, names the specific artefacts that decide whether the recommendation wins the cross-functional review, and ships with the tailored implementation playbook for the buyer's actual portfolio mix.

FAQ

Does the course assume a specific rules engine or model platform?

No. The patterns and artefacts apply across the major rules engines and model platforms in the marketplace and payments space. The implementation playbook is tailored to whichever stack the buyer describes at purchase.

Is this useful if the BNPL or merchant cash advance product is not yet live on the platform?

Yes. Modules 3, 6, and 11 still apply because the chargeback and fraud side of the analyst's seat is the same with or without a credit product, and the BNPL modules become the analyst's preparation for when the product launches.

How is the implementation playbook tailored?

At purchase the buyer describes the portfolio mix, the rough scale, the rules engine and model platform in use, and the specific cross-functional review cadence. The implementation playbook is hand-built against that and delivered alongside course access.

Is there a refund if it is not what was expected?

Thirty day money-back. No conditions other than the buyer telling us why so the course gets better.

30-day money-back guarantee. If after a week of working through the materials this is not what you needed, reply to the receipt email and a full refund is processed. No questions, no forms.

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.