Is your organization struggling with rising defaults and losses? Are manual credit decisions inconsistent and inefficient?
If you need to strengthen risk modelling expertise, this intensive course delivers.
The Credit Risk Modelling Course equips analysts to develop predictive scores and dynamically optimize decisions.
After completing this hands-on training, your team will be able to:
- Engineer insightful risk assessment models
- Automate and accelerate underwriting
- Gauge probabilities of default more accurately
- Price loans commensurate with risk
- Adapt quickly to market changes
- Continually enhance models through machine learning
The Costs of Outdated Credit Risk Practices
Relying on outdated methods can lead to:
- Revenue declines from excessive risk losses
- Missed growth by mispricing profitable applicants
- Margins compressed by poor risk-based pricing
- Inefficient manual underwriting slowing processes
- Subjective inconsistencies in human decisions
- Lack of quant rigor to defend models
- Lagging rivals with advanced analytical capabilities
Typical factors impeding progress include:
- Data scattered across siloed systems
- Overreliance on basic regression techniques
- Lack of statistical and coding expertise
- Absence of model validation governance
- No systematic champion-challenger model process
Why Our Credit Risk Modelling Course Delivers Results
Our transformative training produces results through:
- Experienced Instructors – Our faculty are credit practitioners with hands-on modelling expertise.
- Customized Content – We tailor the curriculum to your data, systems, and needs.
- technical and Practical – We build both hard quant skills and real-world acumen.
- Collaborative Approach – Group projects foster teamwork and applied learning.
- Measurement – We audit abilities before and after through assessments.
- Application – Learners build actual models on your data to cement skills.
Comprehensive Modelling Curriculum
In this intensive course, credit analysts will gain a strong foundation in:
Data Engineering
- Ingesting Data – Connect to structured data sources like databases as well as unstructured data sources like emails, call transcripts, and news.
- Cleaning Data – Resolve errors, fill missing values, smooth outliers, conform formats, resolve redundancies and inconsistencies.
- Blending Data – Join disparate datasets through careful alignment of entities and temporal cutoffs.
Feature Engineering
- Feature Selection – Determine salient drivers of default based on domain knowledge, statistical tests, and machine learning algorithms like regularization.
- Domain Knowledge Encoding – Configure logic reflecting credit policies, economic factors, expert judgment and domain relationships between attributes.
- Feature Construction – Derive new metrics, categories, clusters and transformed variables to capture complex insights.
Model Building
- Algorithm Selection – Choose appropriate machine learning algorithms based on the problem specification, data characteristics, accuracy requirements, interpretability constraints, and deployment considerations.
- Parameter Tuning – Optimize cutoffs, regularization, tree depth, neural architecture, etc. to maximize out-of-sample performance and operational impact.
- Discrimination and Calibration – Assess model separation, precision, recall, lift, response curves, KS-statistic, confusion matrices, and calibration plots.
- Stability Analysis – Quantify variability in performance through bootstrap analysis and other resampling techniques.
Operationalization
- Champion/Challenger Testing – Continually benchmark against alternative models and determine when models drift enough to warrant redevelopment.
- Integration and Infrastructure – Embed models into automated decisioning systems, build platforms to manage models post-deployment with error handling, monitoring, and periodic recalibration.
- Judgment Integration – Blend quantitative insights with human discretionary adjustments grounded in expertise.
Drive Higher Growth, Lower Risk
With advanced risk modelling skills, your team will:
- Enhance Decision Quality – Sophisticated algorithms outperform human subjectivity.
- Accelerate Underwriting – Automated analytical tools increase efficiency.
- Reduce Defaults – Precision scoring identifies and avoids bad risks.
- Grow Profitably – Customized pricing aligns to risk profiles.
- Spot Emerging Risks – Early warning models detect areas of concern.
- Continually Improve – Machine learning provides real-time feedback and adaptation.
Invest in Your Competitive Advantage
In today’s data-driven economy, analytical mastery is a necessity, not luxury.
Transform your risk capabilities and safeguard profits for the long-term by enrolling in our intensive Credit Risk Modelling Course.
We offer in-person and virtual sessions. Contact us today to learn more and register.
Our quantitative experts look forward to helping your analysts master the advanced skills that drive smart growth.