Problem
- Significant churn in existing customer base which limits overall growth ambitions in saturated Swiss market
- Large number of customer data available, which was not systematically analysed
- Client advisors often surprised if customers leave
Churn prevention
Customer
Major Swiss bank
Solution
- Early warning system was created based on a machine learning model to identify customers at risk of leaving or withdrawing a large part of their assets (attrition risk)
- System based on regular monitoring of client behaviour (e.g. transaction behaviour, the intensity of engagement) to predict the attrition risk
- A customer group specific retention approach was developed to pro-actively contact customers with high attrition risk
Impact
- It could be proven that the machine learning model identifies the right customers at risk
- Retention approach has proven to be highly effective since the churn rate of customers at risk could be significantly reduced
- Client experienced strong business benefits, since revenue outflows due to customer churn could be reduced
Client Monitoring
Customer
Large Swiss bank
Problem
- As part of various regulatory requirements, the bank is required to setup monitoring for individuals in order to detect money laundering or clients that might be doing business with undesired parties
Solution
- Definition and implementation of a framework for monitoring of customers for AML and Sanctions use cases across the entire footprint of the bank
Impact
- Vast reduction in the amount of time required to define scenarios, from weeks to minutes
- Improved reliability of scenarios
Residual value prediction
Customer
German automotive
Problem
- Car manufacturer holds large global leasing portfolio that has to be periodically evaluated
- Current valuation predictions are inaccurate
Solution
- Based on a large volume of past transactions (12 years) and cars parameters (model, mileage, options), etc. predict accurately the residual value of the car
Impact
- Accuracy of the valuation improved by $100M’s (from large overall base value)
Financial health watchlist
Customer
Swiss Financial Institution
Problem
- The ability to predict which business partners might be in bad financial health became more complex and volatile during the crisis.
Solution
- Creation of a central gathering and processing platform for the relevant partner data and central risk modelling
- Automated reporting system for the risk evaluation
Impact
- Improved risk management by information centralisation and standardisation
User behaviour monitoring
Customer
Major Swiss bank
Problem
- Impared visibility of user activity and behaviour allows for malicious behaviour to be undetected for a long period of time
- Compliance group in a global private bank decides to monitor user activity on the central data platform
Solution
- Introduction of dynamic altering and anomaly detection system including enhanced signal information via integration of new sources of data (HR data, badge swipes, VPN logs, … )
Impact
- Greatly improved security, audit and monitoring capabilities of the platform team
- Decreased risk of malicious behaviour remaining undetected
Traders monitoring
Customer
International Swiss Investment Bank
Problem
- Increased risk of rogue trading/fraud
- Possible negative impact on bank’s reputation
- High Regulatory cash reserves
Solution
- Machine Learning based automated fraud detection tool to detect rogue trading
Impact
- Minimized risk
- Lowered brand exposure
- Limited the regulatory cash reserves
Financial transactions monitoring
Customer
Global Swiss Pharma producer
Problem
- Customer struggled with detecting anomalous financial transactions – the current process is labour intense and error prone due to mass volume of the transactions and amount of false positive alerts
Solution
- Unsupervised machine learning system to automatically flag anomalous transactions together with prioritisation based on severity and an explanation why a given transaction can be an anomaly
Impact
- Lower need for manual interaction
- More accurate transactions monitoring
- Quicker time to act on an alert
Unified Client View
Customer
International Swiss Bank
Problem
- Siloed data across multiple systems
- Inability to calculate risk exposure
- Limited ability to meet compliance regulations
- Prevents proper AML
Solution
- Creation of a single client view for the bank’s customers
Impact
- Single client view -> visibility
- Ability to calculate risk exposure
- Foundation for AML and future use cases