Debate
Is Machine Learning Mature
Enough to Successfully
Implement in Financial
Institutions
Hernan Huwyler

Prerequisites for implementing machine learning
• Business case with individual processes to automatize
• Hardware to support extensive computations
• Known business rules to develop the learning
• Abundant normalized quality data with correct labels and
noise-free dataset to train
• Structured documents and forms to extract data
• Technical staff capabilities to create and maintain models
• Security control protocols and data governance policies
• Decisions are based on data
• Culture to promote innovation and experimentation

Prerequisites for implementing machine learning
Input
• Collection
• Validation
• Cleansing
• Validation
Training
• Labelling
• Training
• Tuning
• Scoring
Deployment
• Scaling
• Testing
• Tuning
• Versioning
Execution
• Setting
• Training
• Validation
• Monitoring
Code
• Scripts
• Artifacts
Data sources
• Training
• Metadata
Configuration
• Automatic retraining
• Continuous delivery

Tips before implementing machine learning
• Clear strategy and cases to monetize data
• Set measurable goals to reduce costs or increase revenue
• Align the requirements with the business and IT
• Involve data owners and subject matter experts in sales,
marketing, finance, human resources and operations
• Use an agile approach with pilots
• Have a data cleansing project before testing
• Communicate to users how to use the insights provided by
machine learning
• Invest in technical skills
• Learn from deviations between model predictions versus
actual outputs

Requirements for planning
• Data integrity of the input data > acceptable cost and
quality of data by internal and external providers
• Model accuracy and performance > acceptable level of
noise by developers
• Quality evaluation > acceptable validations of outputs by
testers and assurance specialists
• Process flexibility > acceptable level of interactions,
updates, skills, and scalability by stakeholders
• Customer expectations > acceptable adoption and
decision-making by users

Potential risk events
• Missing or inaccurate data to develop training or scale
• Unacceptable false positives and negatives ratios
• Slow or partial adoption of machine learning developments
• Insights not actionable for users
• Behaviors and decisions are not impacted
• Constant model adjustments
• Compliance breaches (particularly in using personal data)
• Potential customers discrimination
• Data invisibles exclusion
• Unpredictable requirements changes

Let´s connect
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hewyler