🏦Credit Risk & Banking Path

The 30+ DPD rate ticked up.
The CRO wants an explanation by close.

Query a consumer-banking data model with planted risk-reporting traps, build the vintage curves, roll-rate matrices, fraud rules, and CECL loss projections that credit-risk, fraud, AML, and regulatory teams actually write every month. Shaped around the Capital One / JPM / BofA analyst interview loop and the work mandated by CCAR, CECL, BSA, and SR 11-7.

Missions in development — landing live nowSee All Missions

25 missions · seed data in development · landing & career guide live now

How It Works

1

Read the briefing

A Slack message from your manager

2

Explore the schema

5 tables in a star schema

3

Write your query

Full SQL editor with autocomplete

4

Get expert feedback

Graduated hints, not just pass/fail

Why This Path

Credit Risk Mechanics

Vintage analysis, roll rates, delinquency bucketing, FICO-band segmentation, and PD/LGD/EAD feature engineering — the queries every credit analyst writes.

Fraud + AML Patterns

Velocity features, structuring detection, rolling-window rules, and composite risk scoring — patterned on NICE Actimize / SAS AML / FICO Falcon workflows.

Regulatory Reporting

Point-in-time quarter-end snapshots, Y-9C / Call-Report-style aggregations, CECL lifetime-loss projection, and PSI / KS model-monitoring queries.

Interview-Ready Skills

Every mission maps to a live Cap One Power Day prompt, a JPM Analyst Development Program case, or a Progressive / fintech fraud-strategy scenario.

The Missions

Each mission is a real request from someone at the company. Difficulty increases as you go.

Starter5 missions
1
Peek at the customer bookFrom: VP Retail Banking
2
What products do we book?From: Chief Data Officer
3
Open accounts right nowFrom: VP Retail Banking
4
Active deposit customers by stateFrom: Head of Consumer Marketing
5
Customer vs account — know the grainFrom: Head of Portfolio Analytics
Easy5 missions
6
Reversals and chargebacks — clean the spend totalFrom: Controller
7
Delinquency bucket snapshotFrom: Head of Collections
8
FICO band distribution at originationFrom: Head of Credit Strategy
9
Approval rate by FICO bandFrom: Head of Credit Strategy
10
Joint accounts — who’s the primary?From: Chief Data Officer
Medium5 missions
11
Monthly net charge-off rate by productFrom: Chief Risk Officer
12
Vintage originations pacingFrom: Head of Credit Strategy
13
Velocity feature — auths in 1 hourFrom: Fraud Ops Lead
14
Structuring flag — sub-$10K cash depositsFrom: BSA Officer
15
Dormant account auditFrom: VP Retail Banking
Hard5 missions
16
Vintage curve — 90+ DPD by MOBFrom: Chief Risk Officer
17
Roll-rate transition matrixFrom: Model Risk Manager
18
Rolling velocity — ≥5 auths in 10 minutesFrom: Fraud Ops Lead
19
Same-day amount-swing flagFrom: Fraud Ops Lead
20
Quarter-end balance snapshot (Y-9C style)From: Regulatory Reporting Lead
Expert5 missions
21
PSI — score distribution vs baselineFrom: Model Risk Manager
22
KS statistic on PD model scoresFrom: Model Risk Manager
23
CECL lifetime loss projection by vintageFrom: Chief Risk Officer
24
Champion–challenger collections backtestFrom: Head of Collections
25
Portfolio risk scorecard capstoneFrom: Chief Risk Officer

The Database

A consumer-banking star schema — customers, accounts, transactions, applications, and monthly delinquency snapshots — with planted risk-reporting traps: reversals that inflate raw spend, customer-vs-account grain gotchas, charged-off accounts still posting recoveries, FICO-at-origination vs current, and banking-day vs calendar-day conventions. Seed data is in development; the mission list and schema shapes above are final.

dim_customers (800)dim_accounts (1,200)fact_transactions (15,000)fact_applications (2,000)fact_delinquency (12,000)

●dimension tables  ● fact tables

Credit analysts don’t get to ship a wrong number

Vintage curves and roll rates land on the CRO’s desk. Learn to write them correctly.

Missions in development

The seed data and mission content ship in a follow-up.

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