Banking risk management is not only the responsibility of the Chief Risk Officer or a specialised risk department.
Risk decisions are made across the organisation.
Credit officers decide whether borrowers should receive financing.
Relationship managers influence the quality of the loan portfolio.
Treasury teams manage liquidity, funding and interest-rate exposure.
Finance teams work with provisioning, capital and financial reporting.
Model developers create analytical models.
Model validators challenge those models.
Internal auditors examine whether controls are operating properly.
Senior management uses risk information to make strategic decisions.
This is why banking risk training for employees should be viewed as an organisational capability programme rather than simply a compliance exercise.
The objective is not to turn every employee into an advanced quantitative modeller.
Instead, employees should receive the level of risk knowledge appropriate to their roles.
A relationship manager may need strong borrower-risk awareness.
A credit analyst may need detailed credit modelling.
A treasury professional may need ALM, liquidity and IRRBB.
A model-validation professional may require statistics, Python and model-governance knowledge.
A senior manager may need to understand risk appetite, capital, stress testing and management actions.
The strongest training framework therefore follows:
Employee Role → Risk Exposure → Required Knowledge → Practical Exercise → Assessment → Application
This guide explains how financial institutions can structure effective banking-risk training for employees, which topics should be included and how practical corporate training can improve risk awareness, analytical capability and decision-making.
What Is Banking Risk Training for Employees?
Banking risk training for employees is structured professional development designed to help employees understand, identify, measure, monitor and manage risks connected with their responsibilities.
Training can range from basic risk-awareness programmes to highly technical modelling workshops.
For example, general banking staff may need to understand:
- Risk ownership
- Operational controls
- Fraud indicators
- Customer-data risks
- Escalation procedures
Credit teams may need deeper knowledge of:
- Financial statement analysis
- Credit appraisal
- Probability of Default
- Credit scorecards
- Portfolio monitoring
- IFRS 9
Treasury teams may require:
- Asset Liability Management
- Liquidity risk
- IRRBB
- Stress testing
- NII
- EVE
The correct structure is therefore role-based banking risk training, not one identical programme for everyone.
Peaks2Tails' current corporate-training offering specifically allows curricula to be customised according to learner requirements, industry needs and training objectives.
Why Banking Employees Need Risk Training
Banks operate by taking and managing risk.
Without risk, there would be little lending, investing or financial intermediation.
The problem is not that risk exists.
The problem is taking risks that are poorly understood, poorly measured or inadequately controlled.
Employees need to understand how their individual decisions can affect the wider organisation.
For example, weak credit assessment can increase future defaults.
Poor liquidity assumptions can create funding pressure.
Incorrect market-risk models can understate potential losses.
Weak operational controls can create fraud or processing losses.
Risk training therefore helps connect everyday employee decisions with the institution's broader risk profile.
Risk Training Should Be Role Based
A major weakness in many corporate programmes is treating every employee identically.
The learning requirements of a relationship manager and a quantitative model validator are completely different.
Training should therefore begin with employee segmentation.
Possible groups include:
- Frontline employees
- Credit teams
- Risk teams
- Treasury teams
- Finance teams
- Model-development teams
- Model-validation teams
- Internal audit
- Compliance
- Senior management
Each group should receive a different depth of training.
Risk Awareness for Frontline Employees
Frontline employees may not need advanced Python models.
They may need to understand:
- Basic credit risk
- Fraud warning signs
- Operational controls
- Data privacy
- Escalation
- Risk ownership
The objective is to help employees recognise risk and respond appropriately.
Risk awareness should become part of normal business behaviour.
Credit Risk Training for Employees
Credit risk is one of the most important risks for lenders.
Employees involved in lending should understand how borrower quality is assessed before and after credit approval.
Training may include:
- Borrower assessment
- Financial statement analysis
- Cash-flow analysis
- Industry analysis
- Credit ratings
- Credit scoring
- Collateral
- Early-warning indicators
- Portfolio concentration
More technical employees may progress into:
- Probability of Default
- Loss Given Default
- Exposure at Default
- IFRS 9
- Stress testing
- Model validation
Peaks2Tails currently includes dedicated credit-risk modelling within its broader banking and risk curriculum.
Credit Appraisal Training
Credit appraisal training should teach employees how to assess whether a borrower has sufficient capacity and willingness to repay.
Important areas can include:
- Business profile
- Management quality
- Financial performance
- Cash flows
- Leverage
- Industry risk
- Existing obligations
- Collateral
A credit decision should not depend on one ratio.
Employees need to understand the complete borrower story.
Financial Statement Analysis for Credit Teams
Credit employees should be comfortable analysing:
- Income Statement
- Balance Sheet
- Cash Flow Statement
Important credit indicators may include:
- Revenue growth
- Profitability
- Leverage
- Liquidity
- Interest coverage
- Cash generation
Strong accounting profits do not automatically mean strong repayment capacity.
Cash-flow analysis remains essential.
Probability of Default Training
Probability of Default, or PD, estimates the likelihood that a borrower defaults during a defined horizon.
Specialist credit-risk employees may learn how PD models are developed from:
- Borrower characteristics
- Repayment history
- Financial ratios
- Behavioural information
Statistical techniques such as logistic regression may then be used to estimate default probability.
LGD and EAD Training
Loss Given Default estimates the severity of loss after default.
Exposure at Default estimates the amount likely to be outstanding when default occurs.
Together with PD, these measures can form the foundation of expected-loss analysis.
Training should teach employees what these measures mean, how they are estimated and where assumptions can fail.
Credit Portfolio Risk Training
Credit teams should also understand portfolio-level risk.
Important areas include:
- Concentration
- Delinquency
- Default rates
- Risk-grade distribution
- Vintage analysis
- Roll rates
A portfolio can contain individually acceptable borrowers and still become risky if exposure is concentrated in one sector or customer type.
IFRS 9 Training for Employees
Finance, credit-risk and model teams may require IFRS 9 training.
Relevant areas can include:
- Expected Credit Loss
- Stage 1
- Stage 2
- Stage 3
- 12-month ECL
- Lifetime ECL
- Significant Increase in Credit Risk
- Forward-looking scenarios
Training should connect accounting requirements with actual model inputs and portfolio behaviour.
Market Risk Training for Employees
Market-risk training is relevant for employees working with financial-market exposures.
Market movements may affect:
- Interest rates
- Equities
- Foreign exchange
- Commodities
- Credit spreads
- Volatility
Specialist training can include:
- Value at Risk
- Expected Shortfall
- Volatility
- Correlation
- Stress testing
- Backtesting
- Sensitivity analysis
The goal should not simply be to calculate a risk number.
Employees should understand what the number does and does not capture.
Value at Risk Training
Value at Risk, or VaR, estimates a loss threshold for a defined horizon and confidence level.
Training may cover:
- Historical VaR
- Parametric VaR
- Monte Carlo VaR
Employees should also understand limitations.
VaR is not a guaranteed maximum loss.
It is a statistical risk estimate.
Expected Shortfall
Expected Shortfall examines losses beyond the VaR threshold.
It provides additional information about severe tail losses.
Training employees on both measures helps them understand why no single metric fully describes market risk.
Market Risk Stress Testing
Stress testing examines severe market conditions.
Possible scenarios include:
- Equity-market decline
- Large interest-rate shock
- Currency depreciation
- Volatility spike
Employees should learn how scenarios are designed, applied and interpreted.
Market Risk Backtesting
Risk models need testing.
VaR backtesting can compare model estimates with realised portfolio outcomes.
Employees should understand:
- Exceptions
- Model performance
- Potential weaknesses
This reinforces an important risk-management principle:
Models themselves create risk if they are trusted without validation.
Treasury Risk Training
Treasury teams manage some of the most important risks on a bank's balance sheet.
Relevant training areas can include:
- Liquidity
- Funding
- Asset Liability Management
- Interest-rate risk
- Stress testing
- Fund Transfer Pricing
Peaks2Tails' current Banking & Risk curriculum contains a dedicated Treasury Risk Modelling component.
Asset Liability Management Training
Asset Liability Management, or ALM, examines how assets and liabilities interact.
A bank may make long-term loans while relying on shorter-term funding.
It may hold fixed-rate assets while deposit pricing changes rapidly.
This creates mismatches.
Employees working in treasury and ALM should understand:
- Maturity gaps
- Repricing gaps
- Funding structure
- Behavioural cash flows
- Liquidity
Liquidity Risk Training
Liquidity risk concerns whether an institution can meet obligations when they fall due.
Liquidity stress can arise through:
- Deposit withdrawals
- Funding-market disruption
- Collateral requirements
- Committed facility drawdowns
Employees should understand how liquidity is measured under both normal and stressed conditions.
LCR and NSFR Training
Relevant teams may need detailed training on:
Liquidity Coverage Ratio
and
Net Stable Funding Ratio.
The objective should not be memorising formulas.
Employees should understand what each ratio is designed to measure and how assumptions affect the result.
ILAAP Training
ILAAP training can be particularly relevant to:
- Liquidity-risk teams
- Treasury
- ALM
- Finance
- Risk management
A practical programme may cover:
- Liquidity governance
- Risk appetite
- Funding concentration
- LCR
- NSFR
- Behavioural deposits
- Stress testing
- Survival horizons
Current Peaks2Tails ILAAP material emphasises cross-functional involvement across risk, treasury, ALM, finance, business, data, technology and audit.
IRRBB Training
Interest Rate Risk in the Banking Book is another specialised area.
IRRBB training can cover:
- Yield curves
- Repricing gaps
- Duration
- Convexity
- NII
- EVE
- Deposit beta
- Deposit decay
- Loan prepayment
- Stress testing
- Hedging
Peaks2Tails currently provides dedicated IRRBB content covering these areas along with Excel and Python modelling.
Net Interest Income Training
Net Interest Income broadly represents:
Interest Income − Interest Expense
Employees should understand how rate changes can affect both components differently.
For example, deposit rates may rise quickly while returns from fixed-rate loans remain unchanged.
This can compress margins.
Economic Value of Equity Training
Economic Value of Equity, or EVE, provides a longer-term economic-value view of interest-rate exposure.
Treasury, ALM and risk teams should understand the difference between:
- NII sensitivity
- EVE sensitivity
These measures answer different questions.
ICAAP Training
ICAAP connects risk with capital adequacy.
Training may include:
- Risk identification
- Capital planning
- Stress testing
- Scenario analysis
- Risk aggregation
- Governance
- Management actions
Finance, risk, capital-management and senior-management teams may require different levels of ICAAP knowledge.
Operational Risk Training
Operational risk can arise from:
- People
- Processes
- Systems
- External events
Every employee contributes to operational-risk management.
Training can include:
- Incident reporting
- Fraud awareness
- Process controls
- Key Risk Indicators
- Escalation
- Business continuity
Operational risk should not be treated as a problem only for risk teams.
Model Risk Management Training
Banks increasingly depend on quantitative models.
These may be used for:
- Credit risk
- Market risk
- IFRS 9
- Treasury risk
- Forecasting
- Machine learning
Model risk can arise because of:
- Incorrect assumptions
- Poor data
- Coding errors
- Overfitting
- Incorrect implementation
- Misuse
Model-development and validation teams therefore require specialised training.
Model Validation Training
Validators need to challenge:
- Methodology
- Data
- Assumptions
- Performance
- Stability
- Limitations
The role of validation is not simply to reproduce the model.
It is to determine whether the model is appropriate for its intended purpose.
Machine Learning in Banking Risk
Machine learning may support:
- Credit scoring
- Fraud detection
- Default prediction
- Customer analytics
- Early-warning systems
But employees should understand model risks such as:
- Overfitting
- Data leakage
- Explainability
- Bias
- Performance drift
Peaks2Tails currently lists Machine Learning among its corporate engagement topics.
Excel for Banking Risk Training
Excel remains valuable across many banking-risk teams.
Applications include:
- Credit models
- Scenario analysis
- Stress testing
- ALM
- Risk reporting
- Model validation
Its transparency makes it especially useful for training because learners can follow calculations step by step.
Python for Banking Risk Training
Python can become important for specialist analytical teams working with:
- Large datasets
- Credit-risk models
- Market-risk calculations
- Machine learning
- Backtesting
- Portfolio analytics
- Automation
The objective should not be teaching Python as an isolated programming language.
Employees should learn Python through banking-risk applications.
Peaks2Tails' wider platform explicitly positions its learning around end-to-end Excel and Python implementations across quantitative and risk modelling.
Practical Training vs Presentation-Based Training
Banking-risk training becomes much more useful when employees actually perform the work.
Practical exercises can include:
- Evaluating a borrower
- Building a credit scorecard
- Calculating PD
- Analysing portfolio concentration
- Calculating VaR
- Performing stress tests
- Building ALM gap reports
- Modelling rate shocks
- Reviewing model documentation
- Using Python on financial datasets
Peaks2Tails' corporate engagement framework emphasises hands-on experience with tools designed to mirror corporate risk-modelling frameworks.
Banking Risk Training Should Use Case Studies
Case studies force employees to apply concepts.
A credit-team case might ask:
Should this borrower receive additional exposure?
A treasury case could ask:
How will the balance sheet react if rates increase?
A liquidity case could ask:
How long can the institution survive a deposit run?
A model-validation case could ask:
Which assumptions should be challenged?
Cases help move learning from theory into judgement.
Skill-Gap Assessment Before Training
Corporate training should begin by determining:
- Existing employee knowledge
- Role responsibilities
- Skill gaps
- Business priorities
- Regulatory needs
- Internal technology stack
This prevents unnecessary training.
A senior credit analyst does not need the same introductory programme as a new management trainee.
Define Measurable Learning Outcomes
Avoid vague objectives such as:
“Understand credit risk.”
A stronger outcome might be:
Employees should be able to identify major borrower-risk drivers and interpret key financial ratios.
For quantitative employees:
Participants should be able to build and interpret a basic PD model using borrower-level data.
Measurable outcomes make assessments meaningful.
Pre-Training Assessment
A short diagnostic assessment can identify employee starting levels.
This helps segment participants into:
- Foundation
- Intermediate
- Advanced
Corporate training becomes much more efficient when employees are not placed into modules that are far below or above their existing ability.
Post-Training Assessment
Training should end with evidence that learning occurred.
Assessment can include:
- Quizzes
- Case studies
- Model-building exercises
- Presentations
- Practical assignments
Peaks2Tails' wider programmes already use exams, quizzes and projects, while its corporate offering emphasises hands-on experience and customised training objectives.
Training for Credit Teams
Credit-team programmes can focus on:
- Financial statement analysis
- Credit appraisal
- PD/LGD/EAD
- Scorecards
- Portfolio monitoring
- IFRS 9
Technical credit-risk teams may add Python and model validation.
Training for Relationship Managers
Relationship managers may need less statistical modelling and more:
- Borrower-risk identification
- Financial analysis
- Industry risk
- Early-warning indicators
- Documentation
The objective is better frontline risk judgement.
Training for Treasury and ALM Teams
Treasury training can focus on:
- Funding
- Liquidity
- ALM
- LCR
- NSFR
- IRRBB
- NII
- EVE
- Stress testing
These topics should be adapted to the institution's actual balance-sheet structure.
Training for Risk Analytics Teams
Risk analytics professionals may need:
- Statistics
- Excel
- Python
- Model development
- Stress testing
- Validation
The curriculum can be significantly more quantitative than broad employee-awareness programmes.
Training for Model Developers
Model-development teams may require:
- Statistical modelling
- Python
- Feature selection
- Calibration
- Documentation
- Monitoring
Machine-learning teams may additionally need:
- Bias testing
- Explainability
- Governance
Training for Model Validators
Validation teams should focus on:
- Independent challenge
- Benchmarking
- Performance testing
- Stability
- Documentation
- Model limitations
The role requires strong quantitative understanding combined with professional scepticism.
Training for Internal Audit
Internal auditors may need enough risk knowledge to evaluate:
- Governance
- Controls
- Model processes
- Documentation
- Regulatory compliance
They do not necessarily need to build every model from scratch.
But they need enough understanding to challenge processes effectively.
Training for Senior Management
Senior-management risk training should focus more heavily on:
- Risk appetite
- Major exposures
- Stress testing
- Capital
- Liquidity
- Model limitations
- Management actions
Executives need to understand enough risk to ask effective questions.
They do not necessarily need detailed Python training.
Physical Banking Risk Training
On-site training can work well for intensive team development.
Benefits include:
- Direct interaction
- Immediate questions
- Group exercises
- Workshops
- Team discussion
This format can be especially useful for:
- Credit appraisal
- IRRBB
- Basel
- IFRS 9
- ICAAP
- ILAAP
Online Training
Online training can support:
- Distributed teams
- Flexible scheduling
- Repeated learning
- Scalability
Technical training can also be delivered online when employees have access to suitable datasets, Excel files or coding environments.
Hybrid Banking Risk Training
Hybrid training can combine:
- Self-paced theory
- Live instructor sessions
- Practical exercises
- Projects
This can be particularly effective for working employees who cannot attend long continuous classroom programmes.
Customised Corporate Banking Risk Training
Banks do not have identical training requirements.
A retail-focused lender differs from:
- Corporate bank
- NBFC
- Fintech lender
- Housing-finance company
- Investment institution
The curriculum should reflect:
- Business model
- Products
- Employee roles
- Existing risk framework
- Technology
- Regulatory environment
Peaks2Tails explicitly states that its corporate curriculum can be customised around learner and industry needs.
Banking Risk Training for NBFC Employees
NBFC employees also need strong risk capability.
Relevant topics can include:
- Lending risk
- Portfolio quality
- Liquidity
- Funding
- Operational risk
- Stress testing
The exact regulatory and balance-sheet framework may differ from banks, so training should be adapted accordingly.
Banking Risk Training for Fintech Employees
Fintech lenders may require particular emphasis on:
- Digital credit
- Data-driven underwriting
- Machine learning
- Fraud detection
- Model risk
- Data governance
Again, the objective should be role-specific training rather than generic banking content.
Corporate Training at Peaks2Tails
Peaks2Tails currently positions its corporate engagement programme around training, mentoring and consulting for financial-risk applications. Current engagement areas listed by the provider include Basel, IFRS, ICAAP, ILAAP, IRRBB, Model Risk, Market Risk, Valuations, Credit Analysis and Machine Learning.
Its corporate page also states that curricula can be customised and that learners can receive hands-on experience using proprietary tools designed to reflect corporate risk-modelling frameworks.
The broader Peaks2Tails platform supports specialist learning across credit risk, market risk, treasury risk, quantitative finance and machine learning, with end-to-end Excel and Python implementations.
This provides a useful foundation for designing separate learning tracks for general employees and specialist risk teams.
How to Design a Banking Risk Training Programme
A structured programme can follow several stages.
First, identify employee groups.
Then assess existing capability.
Define training outcomes.
Map relevant risks to each role.
Choose an appropriate delivery format.
Combine theory with exercises.
Assess performance.
Finally, identify areas requiring follow-up training.
The process can be summarised as:
Roles → Skill Gaps → Curriculum → Practical Exercises → Assessment → Follow-Up
Example Foundation Programme
A foundation programme for general bank employees might cover:
- Banking-risk fundamentals
- Credit risk awareness
- Operational risk
- Fraud awareness
- Risk ownership
- Escalation
- Basic regulatory awareness
The objective is broad risk culture.
Example Specialist Programme
A specialist programme for risk teams might include:
- Credit-risk models
- Market risk
- Stress testing
- Treasury risk
- Model validation
- Excel
- Python
The objective is technical capability.
Example Management Programme
A senior-management programme can focus on:
- Risk appetite
- Capital adequacy
- Liquidity
- Stress testing
- Model risk
- Governance
- Management actions
The objective is stronger oversight and decision-making.
Benefits of Banking Risk Training for Employees
Effective training can support several organisational goals.
It can help improve:
- Risk awareness
- Credit judgement
- Model understanding
- Cross-functional communication
- Regulatory readiness
- Decision quality
But training should not be evaluated only according to attendance.
The real question is whether employee capability changed.
Better Risk Culture
Risk culture develops when employees understand that risk management is part of their own responsibilities.
It should not be seen as something owned only by the central risk function.
Relationship managers, credit officers, treasury staff, operations teams and management all influence risk.
Training helps create this shared understanding.
Better Communication Between Teams
Risk terminology can create barriers between:
- Business
- Finance
- Treasury
- Risk
- Technology
Structured training can create a more consistent vocabulary.
This helps teams discuss models, assumptions, limits and risk scenarios more effectively.
Better Model Understanding
Employees should not blindly accept system outputs.
Training can help them ask:
What assumptions drive this model?
What data was used?
What are the limitations?
When might the result become unreliable?
These questions are especially important as banks use more advanced models and AI.
AI in Employee Risk Training
AI increasingly affects banking workflows.
Employees may use AI to:
- Analyse data
- Generate code
- Summarise documents
- Create reports
Risk training should therefore address:
- Validation of AI output
- Data confidentiality
- Model risk
- Bias
- Explainability
- Governance
AI can increase productivity.
It can also create new risks if employees trust generated outputs without review.
Continuous Risk Training
Risk education should not be treated as a one-time activity.
Banking changes.
Regulations change.
Models change.
Technology changes.
Economic conditions change.
Employees therefore need periodic refreshers and deeper specialist modules as their responsibilities evolve.
How to Choose a Corporate Banking Risk Training Provider
Organisations should look beyond a generic training catalogue.
A credible provider should be able to:
- Understand the institution's business
- Customise the curriculum
- Adjust depth by role
- Use practical examples
- Provide technical modelling where necessary
- Assess learning outcomes
The key question is:
Can the provider translate risk theory into employee capability relevant to our organisation?
Common Mistakes in Corporate Risk Training
One common mistake is giving everyone the same presentation.
Another is measuring success purely through attendance.
Other problems include:
- Too much theory
- No practical exercises
- No assessment
- No role segmentation
- No follow-up
- No connection with real business decisions
Effective risk training should be designed around capability.
Frequently Asked Questions
What is banking risk training for employees?
It is structured professional training designed to improve employee understanding of the banking risks relevant to their roles.
Which employees should receive banking-risk training?
Training can be relevant to frontline employees, credit teams, treasury professionals, risk analysts, model developers, validators, finance teams, internal audit and senior management.
What topics can be included?
Topics may include credit risk, market risk, liquidity, ALM, IRRBB, ICAAP, ILAAP, operational risk, model risk, IFRS 9, Excel, Python and machine learning.
Can banking-risk training be customised?
Yes. Corporate training is generally more effective when it is adapted to employee roles, the institution's products and its specific risk environment.
Should every employee learn Python?
No. Python is more relevant to quantitative, analytics and modelling teams. Other employees may require stronger risk awareness and financial judgement instead.
Can Excel be used in banking-risk training?
Yes. Excel is useful for transparent financial and risk modelling, scenario analysis and model demonstrations.
Is banking-risk training relevant to NBFCs?
Yes. NBFCs also face credit, liquidity, operational, model and portfolio risks, although their specific regulatory requirements and business models may differ.
How should training effectiveness be measured?
Organisations can use assessments, practical exercises, case studies, presentations and post-training performance evaluation rather than attendance alone.
Conclusion: Banking Risk Training for Employees Should Build Real Organisational Capability
A bank can have sophisticated risk policies.
It can purchase expensive systems.
It can build advanced models.
It can establish multiple risk committees.
But none of these mechanisms works effectively if employees do not understand the risks connected with their decisions.
That is why banking risk training for employees should not be treated merely as a mandatory annual exercise.
The objective should be capability.
A relationship manager should recognise borrower deterioration.
A credit analyst should understand financial statements and risk drivers.
A market-risk analyst should understand both VaR and its limitations.
A treasury professional should understand liquidity, ALM and IRRBB.
A model developer should understand assumptions, calibration and model limitations.
A validator should know how to challenge methodology independently.
An internal auditor should understand enough risk to assess governance and controls.
A senior manager should be able to challenge risk reports and understand the implications of stress scenarios.
The strongest corporate learning framework therefore becomes:
Role Identification → Skill-Gap Assessment → Relevant Risk Training → Practical Application → Assessment → Continuous Development
Different employees require different levels of depth.
Broad employee populations may need strong risk awareness.
Credit teams may need borrower and portfolio analysis.
Treasury teams may need ALM, liquidity and IRRBB.
Quantitative teams may need Excel, Python, statistics and model validation.
Senior management may need integrated risk, capital, liquidity and governance training.
Peaks2Tails' current corporate engagement framework aligns with this role-based approach through customisable training across Basel, IFRS, ICAAP, ILAAP, IRRBB, model risk, market risk, credit analysis and machine learning.
Its wider risk-modelling ecosystem also supports deeper specialist tracks in credit, market and treasury risk with practical Excel and Python implementation.
For banks, NBFCs and other financial institutions, the important question should therefore not simply be:
“Have our employees completed banking-risk training?”
The stronger questions are:
Can they recognise the risk?
Can they measure or interpret it appropriately?
Can they challenge questionable assumptions?
Can they escalate problems early?
Can they make better decisions because of what they learned?
When the answer to those questions improves, banking risk training has moved beyond compliance and started building genuine organisational capability.