The document discusses how emerging technologies can disrupt credit risk management by 2025, noting banks will need fundamentally different risk functions to handle new demands. It describes what credit risk management is and some ways emerging technologies like machine learning, analytics tools, and interactive insights bots could be leveraged to perform deep 6W analysis, zero-touch forecasting, monitoring, and "what-if" scenario modeling at scale to help risk managers address what is at stake. Sample interactions with an interactive insights bot are provided to demonstrate how it could provide executives quick insights and predictions by feature in response to natural language requests.

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Disrupting Risk Management Through
Emerging Technologies

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“By 2025, risk functions in banks will need
to be fundamentally different than they
are today. Unless banks start to act they
may be overwhelmed by the new
demands they will face.”
– McKinsey and Co. The Future of Bank Risk Management

4. What is Credit Risk Management ?
● Credit risk is the risk arising due to the borrower’s failure to strictly comply with
the terms of the credit contract. This might happen when the customer is late in
debt repayment, not fully pays the debt amount or fails to pay debt when
principal and interest amounts are due, causing financial losses and difficulties
in the business activities of banks
● Credit risk management is a process of identifying and analyzing risk factors,
measuring the level of risk, thereby selecting measures to manage credit
activities to limit and eliminate risks in the credit process.

5. Acceleration of in-depth 6-W analysis: what, who, when, where, why
and what-if
- Perform in-depth analysis to answer
even the deepest analytical questions.
- Leveraging Emerging Technologies to
identify portfolio level shifts
- Drill down into individual portfolios and
perform policy based “what if analysis.”

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Bring together the full suite of traditional model development,
execution, analysis and machine learning tools
Zero-touch execution
Actuals, and original expectations
Vertical and horizontal forecasts
Loan level Visualization
Report refresh in milliseconds
Powerpoint in minutes
Machine Learning
Dynamic features and competing models
Early trend detection
Monitoring
Data quality, actuals, model health, and
the economy in one place
What-if analysis
Input, assumption and scenario gaming to
answer common what-ifs
No-code data and models
New feature exploration in minutes

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What is at stake?
Ease of Use
Easy to use “on the
go”; quickly check
metrics and execute
hypothetical
scenarios between
meetings
Push-Button
Forecasting
Execute multiple
competing models at
the push of a button.
Instantly view high
level results and drill
down as needed
Deep Analytics and
Modeling
Leverage powerful
machine learning
tools to perform
“what if” analysis
and analyze the
newest hypotheses
Security
Secure and
compliant data,
following company-
wide privacy policies
Outside Support
Chat capabilities with a
loss forecasting expert
when assistance is
needed

8. Imagine if we can make this interactions ...
Executives or Decision makers request
for some insights to get predictions for
new risk insights using new features
available
The sequence of interactions taking place
to get the insights generated and shipped
back to decision makers

9. to this ...
Executives or Decision makers request
for some insights to get predictions for
new data
get insights at your fingertips

10. Interactive Insights Bot
Interactive execution
Actuals, and Predictions forecasts
Charge Off Level Visualizations
graph insights refresh in seconds in the
Bot
Machine Learning
Dynamic features added on the fly to
compute the models
Magnitude of Data Used
Terabytes to Petabytes of data mined
and creating features using them
Multiple Features influence
Predictions
Ability to influence the forecasted
trajectory based on multiple features used
on fly

11. Interactive Insights Bot Architecture

12. Interaction Workflow
run charge-off
show charge-off
AWS Lambda ML ModelAWS SQS
S3
Publish the
Dataset
Publish Model
Execution Result to S3
Bucket
Retrieve graph
from S3 bucket
DAs to use Databricks to
analyse the datasets

13. Analytics (Databricks
for Inputs/Outputs)
● In order to successfully pull this off, we needed a robust tool
for analytics and data prep
● Databricks was the tool of choice
● Data prep code and analytics leveraged Python databricks
notebooks to get the job done.
● Automatically trigger operations on the notebooks based on
the initial filtering
● Visualizing the data trends on the notebook cells to see the
graph data
This is all sample and mock data only

14. @risk-ml-bot help
This is all sample and mock data only

15. @risk-ml-bot show actuals by feature
This is all sample and mock data only

16. @risk-ml-bot run w/ addFeature
This is all sample and mock data only

17. @risk-ml-bot show predictions by feature
This is all sample and mock data only

18. Demo

20. Feedback
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