Credit Risk Analyst
89% skill overlap
Your role
Credit Risk Model Developer
Full profileYour role
- L3 salary
- Not published yet
- Upcoming dated changes
- 12 changes ahead
What the role does
A credit risk model developer builds and maintains the statistical models that estimate credit risk: probability of default (PD), loss given default (LGD) and exposure at default (EAD). These models feed capital requirements under the IRB (internal ratings-based) approach of the Basel framework, expected credit loss under IFRS 9 (International Financial Reporting Standard 9), stress tests and lending decisions. The role prepares data, selects and estimates models, documents them for validation and supervisors, and monitors their performance.
Read a day in the life
A day in the life
A typical day mixes data work, modelling and documentation. In the morning the developer may check the monthly model monitoring results: are default rates in each rating grade in line with predicted PDs, has the population shifted, does the model still rank borrowers well. A drop in performance is investigated and noted. Then comes development work on a model under redevelopment, for example an LGD model for mortgages. The developer prepares the default and recovery data, handles missing values and outliers, tests candidate drivers such as loan to value and region, and estimates models in Python, R or SAS. Results are compared on discrimination, calibration and stability. After lunch there may be a meeting with model validation to answer their findings, or with credit officers to check that model drivers make business sense. Much of the afternoon goes to documentation, which must allow an independent reviewer to replicate every step.
Levels
- L1 Starting out
- L2 Independent
- L3 Senior or specialist
- L4 Leads a team or an area
- L5 Leads the function
Full level descriptions and job titles
The EFO level names are the common scale; job titles differ between employers.
| Level | What the work looks like | Typical years | Typical job titles |
|---|---|---|---|
| L1 Starting out | Prepares modelling datasets, runs monitoring and back-testing scripts, documents results and learns PD, LGD and EAD methods, IRB and IFRS 9 rules under supervision. | 0-2 | Banks: Analyst, Junior Analyst, Associate (asset servicing), Trainee, GraduateAsset servicers: Junior, Specialist (entry), Officer |
| L2 Independent | Develops model components or simpler models end to end, writes documentation, answers validation questions and trains new team members on code and data. | 2-5 | Banks: Analyst / Senior Analyst, AssociateAsset servicers: Specialist, Senior Specialist, Senior Officer |
| L3 Senior or specialist | Leads development of complex models, chooses methodologies, handles regulatory and validation challenges and represents modelling in data, system and supervisory projects. | 5-9 | Banks: Senior Associate, AVP, Assistant ManagerAsset servicers: Senior Specialist, Expert, Team Lead (small team) |
| L4 Leads a team or an area | Leads a model development team, owns a portfolio of models, the development standards and the plan, and is the escalation point for model findings and regulatory reviews. | 8-14 | Banks: VP, Manager, Senior ManagerAsset servicers: Team Lead, Manager, Head of Section |
| L5 Leads the function | Accountable for credit risk modelling in a legal entity or region, sets modelling strategy and standards, and presents model matters to risk committees and supervisors. | 12+ | Banks: Director, Executive Director, Managing Director, Head of functionAsset servicers: Head of Department, Director |
Skills
Where the skill weights come from
Each role lists its skills with a tier and a weight. The weights of one role add up to 100 percent.
Source: the EFO role catalogue, built from the ESCO classification and reviewed by the editors.
The percentage is the skill's share of the role; the bars are drawn relative to the anchor skill, which is shown full.
Critical
- Credit risk models (IFRS 9, IRB)Weight 13.6%Relevant to you
- Credit analysis and creditworthinessWeight 9.1%Relevant to you
- Financial statement analysis and ratiosWeight 9.1%Relevant to you
- Basel framework and capitalWeight 9.1%Relevant to you
- Applied statistical modelling for pricing and risk (GLM, survival)Weight 9.1%Relevant to you
Important and supporting skills 16
Important
- Descriptive statistics and distributionsWeight 4.6%Relevant to you
- Advanced Microsoft ExcelWeight 4.6%Relevant to you
- Risk judgment and escalation instinctWeight 4.6%Relevant to you
- Professional written communicationWeight 4.6%Relevant to you
- Querying data with SQLWeight 4.6%Relevant to you
- Attention to detailWeight 4.6%Relevant to you
- Prudential and regulatory reporting (COREP, FINREP, US call reports)Weight 4.6%Relevant to you
- Control mindset (what could go wrong)Weight 4.6%Relevant to you
Supporting
- Financial markets knowledgeWeight 2.3%Relevant to you
- Statistical software (SAS, R)Weight 2.3%Relevant to you
- Working to tight deadlinesWeight 2.3%Relevant to you
- Derivatives and how they workWeight 2.3%Relevant to you
- Assessing the quality of sourcesWeight 2.3%Relevant to you
Useful
- Python for data analysisWeight 0.8%Relevant to you
- Stakeholder managementWeight 0.8%Relevant to you
- International accounting standards (IFRS)Weight 0.8%Relevant to you
Processes
The work the role takes part in.
- Capital calculation
- Credit risk and lending decisions
- Insurance underwriting, claims and policy
- Model development and validation
What each process covers
- Capital calculationMain
- Credit risk and lending decisionsMain
- Insurance underwriting, claims and policyMain
- Model development and validationMain
- Prudential reportingSupporting
Where the role concentrates
How AI is changing the role
Code assistants speed up data preparation, testing and documentation, and machine learning techniques such as gradient boosting are used to find drivers and as challenger models.
Read the full assessment
Code assistants speed up data preparation, testing and documentation, and machine learning techniques such as gradient boosting are used to find drivers and as challenger models. The European Banking Authority has examined machine learning for IRB models and stresses explainability and governance, and the EU AI Act treats credit scoring of individuals as high risk. The World Economic Forum Future of Jobs Report 2025 lists AI and big data as the fastest-growing skill. Value moves to sound methodology, explaining models to validators and supervisors and judging when a model no longer fits.
Dated changes for this role
- Second Consumer Credit Directive appliesRelevant to you
- Swiss capital and liquidity ordinance changes from January 2027Relevant to you
- CRR3 output floor rises to 60 percent in 2027 and 65 percent in 2028Relevant to you
- FINMA circular on nature-related financial risks phases inRelevant to you
- UK Basel 3.1 rules take effectRelevant to you
Worth knowing
Salary snapshot
Salary figures are not published yet
The L3 salary range for this role, with its sources, will appear here when the salaries section opens.
Certifications
- FRM
- PRM
- CFA Program Level I
- ACCA Qualification
Concepts to know
- Expected credit loss (ECL)
- IFRS 9 Financial Instruments
- Machine learning model
- Model validation
- Probability of default (PD)
- Backtesting
- CRR3
- Definition of default
All 24 concepts
- Discriminatory power (Gini, AUC)
- Explainability (AI)
- Exposure at default (EAD)
- High-risk AI system
- IFRS 9 staging
- IRB approach (internal ratings-based)
- Loss given default (LGD)
- Model drift
- Model inventory
- Model risk
- Output floor
- Pillar 1 (minimum capital requirements)
- Rating model (internal credit rating)
- Risk-weighted assets (RWA)
- Significant increase in credit risk (SICR)
- Stress test (supervisory)
Neighbouring roles
Roles that need many of the same skills, by weighted skill overlap, with the skills you would need to add.
Corporate Credit Analyst
85% skill overlap
To add: Corporate governance
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Merchant Underwriting & Risk Analyst
70% skill overlap
To add: Merchant acquiring and payment processing, Know your customer due diligence, Fraud typologies and controls
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Underwriting Analyst
54% skill overlap
To add: Insurance pricing, reserving and product economics, Insurance regulation (Solvency II, IDD, NAIC, IRDAI), Reading client needs and dynamics
Your role
Market Risk Analyst
51% skill overlap
To add: Risk measures and sensitivities, Model validation and governance, Independent price verification
Your role
ALM Analyst
50% skill overlap
To add: Asset-liability management and rate risk, Liquidity metrics (LCR, NSFR), Treasury and cash flow management
Your role
Do this job? Suggest a correction
Sources
- ESCO occupation: credit risk analyst (external site)
- ESCO occupation: financial risk analyst (external site)
- European Banking Authority, Special topic: artificial intelligence (external site)
- GARP, Financial Risk Manager (FRM) certification (external site)
- World Economic Forum, Future of Jobs Report 2025 (external site)
Probability of default (PD)Probability of default is the likelihood that a borrower defaults over a given horizon, usually one year. Capital models use a long-run average (through-the-cycle) PD per rating grade, while IFRS 9 uses point-in-time 12-month and lifetime PDs that react to the economic outlook.
Loss given default (LGD)Loss given default is the share of an exposure a lender expects to lose if the borrower defaults, after recoveries from collateral and workout, and net of costs. Capital models use a downturn LGD, while IFRS 9 models use a forward-looking LGD.
Exposure at default (EAD)Exposure at default is the amount a lender expects to be owed when a borrower defaults. For loans with undrawn limits, such as credit cards and revolving facilities, it adds the expected extra drawing through a credit conversion factor (CCF).
Expected credit loss (ECL)Expected credit loss is the probability-weighted estimate of credit losses over a period, the basis of IFRS 9 loss allowances. It is usually computed as PD x LGD x EAD, discounted at the effective interest rate, over 12 months for Stage 1 and over the lifetime for Stages 2 and 3.
Model validationModel validation is the independent review that checks whether a model is conceptually sound, built on suitable data, correctly implemented and performing as intended for its use. It is done before first use and periodically after, and its findings feed model approval and the model risk framework.
Explainability (AI)Explainability is the ability to describe, in terms a person can understand, why a model produced a given output, for example which factors drove a credit decline. It supports validation, human oversight, customer explanations under the GDPR and AI Act transparency duties.
AI Act (Artificial Intelligence Act)The AI Act, Regulation (EU) 2024/1689, is the EU law that sets rules for AI systems according to their risk: some practices are banned, high-risk systems face strict requirements, and general-purpose AI models have transparency duties. Credit scoring of individuals is a high-risk use.