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

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)

Churn prevention

Customer

Major Swiss bank

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

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

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

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