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

Challenge

• Difficulties maintaining code and monitoring data health
• Making assumptions leading to incorrect outputs
• Need to reimplement existing pieces of software
• Big overhead to integrate new data sources for each use-case

Solution

• Trusted, opinionated and reliable code assets that define guidelines and enforce compliance
• Code modules that allow downstream users to consume and interpret data assets

Impact

• Reduced time to onboard new data assets
• Reduced human errors thanks to guideline enforcement
• Increased transparency about availability and content of data assets
• Ensured common understanding of the data model, allowing to scale data asset development efforts

Designing and integrating Tableau to enable secure access to datasets stored on the main Foundry instance

Challenge

• Need to enable a wide pool of users to have access to vast datasets stored in a Foundry instance

Solution

• Designed and integrated a Tableau server with Postgres and Postgate
• Foundry datasets can now be accessed through tableau while preserving security standards present in Foundry

Impact

• More than 300 Tableau developers and users now able to securely access and consume the datasets directly from Tableau

Optimising platform performance with a set of tools and best-practices combined with coaching, resulting in lower costs and smoother user experience

Challenge

• Platform adoption skyrocketed and rate of increase is higher than what new hardware added is capable of handling
• Usage expected to keep climbing as more projects are being onboarded, leading to further cost increase and performance degradation

Solution

• Established performance metrics baseline
• Coached client’s engineers to enable them to improve platform performance
• Created tooling and reports to pinpoint performance problems and allow self-service improvements

Impact

• Reduction in new hardware addition and maintenance costs
• Increase in platform performance resulting in smoother user experience

Creating a dashboard unifying all trading information resulting in a more holistic understanding for credit risk officers and substantial risk reduction

Challenge

• Credit risk officers struggle to understand precisely who they are trading with, what kind of goods are traded, and what are the exposure concentrations

Solution

• Created a dashboard presenting an interactive view of the CTF portfolio and its clients to mitigate risks and recognize business opportunities
• Entity resolution (based on free text sources) to provide a unified view of all external parties involved in the transactions

Impact

• More holistic understanding of CTF business across departments
• Substantial risk reduction through better understanding of exposures

Defining and implementing a surveillance framework to identify illegal client behaviour resulting in greatly improved ability to meet surveillance requirements

Challenge

• Regulations require the client to setup surveillance for money laundering and illegal activities
• Disparate data sources, and diversity in regulations between countries
• Inability to monitor client behaviour to spot illegal activities

Solution

• Leveraging a previous project Unit8 conducted (Single Client View), built a generic framework enabling data scientists to easily define surveillance scenarios to monitor potential high-risk client activities

Impact

• Greatly reduced amount of time needed to define surveillance scenarios, from weeks to minutes
• Reduced resources required and build-time of pipelines by more than 50%
• Improved reliability of scenarios

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)

Developing a centralised solvency and risk assessment model to create risk exposure reports automatically and improve data quality

Challenge

• Difficulty to predict total exposure to financial losses resulting from companies at risk of becoming insolvent
• Current risk reports are generated manually and data quality is not guaranteed

Solution

• Central gathering and processing of the relevant partner data allowing for central risk modelling and data quality checks
• Automatic creation of monthly risk reports based on the curated data

Impact

• Improved risk management capabilities through information centralisation and standardisation
• Improvement in data quality and report generation
• Possibility to create reports dynamically to visualise more aspects than before

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

Developing an ML model to identify potential rogue traders and flag them to minimise risk of financial losses and lower need for regulatory cash reserves

Challenge

• Inability to monitor and identify traders whose behaviour differs from what is expected, resulting in potential monetary and reputational risks for the client

Solution

• Integrated trading data from several sources into a single platform
  Deployed ML techniques to identify potential rogue traders based on their trading behaviour
• Configured an alerting system to flag traders with an elevated risk of rogue trading

Impact

• Able to quickly identify risky traders based on their trading activities, which would have been impossible to do manually
• Minimised rogue trading risk and potential financial losses, lowering the need for regulatory cash reserves

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

Creating a single client view for an international Swiss bank allowing them to track and compute total risk exposure, and meet compliance regulations

Challenge

• Siloed data across multiple systems
• Inability to calculate risk exposure
• Limited ability to meet compliance regulations
• Sub-optimal conditions to apply proper AML

Solution

• Aggregated, cross-referenced and combined data from hundreds of disparate data sources
• Used ML to automatically surface high-risk clients
• Implemented a scalable solution to account for future needs

Impact

• Achieved single client view, increasing visibility
• Able to calculate overall risk exposure
• Compliance regulations can be met