About this report
With the apparent advantages of using data-driven insights to develop their business, many wealth managers are beyond the point of just thinking about data science. However, many of them get confronted with the complexity and incalculable difficulties of AI projects rather sooner than later.
This report highlights the strains and prospects of a data science implementation project and helps wealth managers focus on tangible business benefits with three practical use cases of data-driven solutions.
Get started with data science
How wealth managers use their data will define their competitiveness. Existing AI implementations unlocked a significant growth potential for financial institutions.
"Many wealth managers envisage a data science project as a straightforward, simple undertaking. The reality, however, is quite different."
3 practical use cases to successfully adopt data science
Case Study 1
Helping compliance officers fight the many false positives and focus on real risks
Case Study 2
Helping relationship managers better understand their clients and identify prospects
Case Study 3
Networks could help avoid the next financial crisis and enable credit risk officers spot aggregated risks
"By combining a user-centric approach with an experienced platform partner and a deep learning capability, wealth managers can overcome the complexity of data science projects and deliver real value fast."
Our 3 steps for wealth managers to better exploit their data
- Build a strategic AI roadmap following a user-centric approach clustered in different business domains and driven by clear use cases.
- Overcome integration complexity with a powerful, experienced service partner to keep costs and project value at a balance.
- Make sure your solution profits from the deep learning effect which requires access to lots of diverse training data.