Designed and deployed a machine learning model to predict
high-risk corporate and investment banking clients. The model
input consisted of multiple internal and external data sources.
The results are then visualised on an interactive dashboard. The
input data and the explanation of the prediction are all
included in the dashboard.
The model is able to highlight the distressed clients 5 months before they default. With the risk managers, the model reduces unexpected losses across credit products. The dashboard enables relationship managers to transition from reactive to proactive risk management.
Designed and implemented a near real-time system to predict
business leads. The machine learning model notifies the client
manager if a news article indicates a business opportunity.
The results are then tailored to the client managers based on
their portfolio and clientele.
This reduces the time spent on manually curating news articles from multiple sources, whereby empowers the client managers to be more proactive in business development.
Implemented a forecasting model to assist with budgeting and
assigning sales targets. The model uses financial drivers to
forecast the closing balance, interests generated and fees
earned for the period.
This assists with the monthly budget planning and the subsequent sales target alignment. As a result, the finance managers and regional managers are able to have a more fruitful performance discussion
Designed and implemented an augmented analytics solution
within the finance dashboard. Using advanced analytics,
insights are embedded within the dashboard used across the
The dashboard enables finance managers to spot anomalies or opportunities easily, and empowers them to better support their business partners.