This tech talk was aimed at providing understanding of the process of model development in the credit scoring and fraud detection domain specifically. It also covers the theme of model retraining. The talk was presented to my then colleagues at SynergyOne.

1. Future models. Strategy
of models retraining.

2. Why do we need Machine Learning
● We cannot program all the possible cases
● Many cases and behavioral patterns are unknown
● Customers behavior changes over time

4. Reasons to retrain models

5. Batch Learning
Model
development
in research
phase
Model
deployment
(OneKarma
Scoring
Service)

6. Online Learning

7. Online Learning vs Batch Learning
Input One application at a time Batch of applications
Concept Model is updated each time an
application processed
Model is updated by request
(depends on batch size)
Performance issues Re-training model is time and power
consuming
Process batch of applications,
re-training model is time and
power consuming
Cons
-
No control over learning process, hard
to interpret results, requires automation
of analytics
Requires calculating features
for a batch of applications
more detailed comparison

9. Fraud Fingerprint
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10. Debt Collection
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11. Scoring LN

12. High expectation customers

13. Cash flow model

14. Thank you for
your attention