Decision Support
❯
Predict
❯A fairer and more personalised credit decision
A fairer and more personalised credit decision
For:
Payment and credit providers
Goal:
Improved Customer Experience
Moving Beyond Generalised Linear Models: A sophisticated pricing strategy incorporating competitor pricing
For:
Customer pricing
Actuaries
Goal:
Increase Revenues
Machine-learning based triage to determine low-severity patients that can be fast-tracked to admission in ED due to their short discharge length
For:
Emergency Departments in hospitals.
Goal:
Improved Employee Efficiency
Disaster and Emergency Prediction & Impact Model
For:
National-level disaster management professionals, Climate change adaptation experts, Government agencies, At-risk communities
Automated threat enrichment intelligence in Cyber security
Goal:
Improve Operation Efficiency
Deep reinforcement learning for personalized treatment recommendation
For:
Clinicians who prescribe long-term and adjustable treatments
Goal:
Improved Employee Efficiency
Applying machine learning to predict patient risk for in-hospital new infections
For:
Hospitals
Goal:
Improved Customer Experience, Anticipate Risks
Large Companies
Small Companies
Management - Market Intelligence
For:
Product managers and Marketers.
Scope:
Growing active users for a music streaming service by identifying high-value customer behavior.
Goal:
Improved Customer Experience, Increase Revenues
Weather Forecasting in Agriculture
For:
Farmers
Goal:
Anticipate Risks, Improve Operation Efficiency
AI for customers' loss prevention of telecommunication services
For:
Telecommunication companies
Goal:
Improved Customer Experience
Large Companies
Manufacturing and Factories – Predictive Maintenance
For:
Operation, R&D
Scope:
Avoiding unplanned shutdowns in Manufacturing using Machine Learning to predict failure states in equipment.
Goal:
Anticipate Risks, Improve Operation Efficiency
Disaster and Emergency Prediction & Impact Model
For:
National-level disaster management professionals, Climate change adaptation experts, Government agencies, At-risk communities.
A fairer and more personalised credit decision
For:
Payment and credit providers
Goal:
Improved Customer ExperienceProblem addressed
Traditionally, banks make their credit decisions based on demographic signifiers and questionnaires to understand an applicant’s income and loan service capabilities. However, these demographic signifiers and questionnaires do not necessary reflect the applicants’ actual financial behaviour. The traditional credit scoring system also lacks of transparency.
Open banking has now made it possible to model actual financial behaviour based on transactional banking data. To do this, a new credit decisioning model is needed to meet unique needs of loan applicants and overhaul the lending system of the French financial industry.
Description
To achieve these goals, Algoan used Kubernetes Engine to build a unique credit scoring platform that allows them to
- access raw banking transaction data via open banking,
- crunch through high volumes of banking transaction data,
- develop new and more sophisticated ML models for credit scoring.
This new platform allows bank assess tailored loan application decisions based on customers’ actual banking transaction data for individuals. Compared to the traditional method of calculating credit scores based on questionnaires, new credit ML models are more powerful and personalised for loan applicants.
Outcome
A fairer and more personalised credit decision making that is reflected on the complex financial needs in 21st century.
Raw Data
AI: Perceive
Machine Learning
AI: Understand
Predict / Forecast
AI: Act