Dataiku published an e-book representative of the use cases for artificial intelligence (AI) and advanced analytics in financial services. From risk management and operational efficiency to environmental, social and governance (ESG) investing, there are dozens of ways banks can harness AI, data, and analytics to drive value at scale — all while minimizing costs and saving time. Dataiku runs through many of the solutions and applications that have helped firms get ahead on their data game.
A few highlighted observations from the book:
Intraday liquidity forecasting
Data pipelines and machine learning models allow data teams to simultaneously analyze external market data — like equity indices, foreign exchange (FX) rates, and yield curves — and internal residual balance and trade settlement data to forecast expected cash flows, set up an early warning system of cash surpluses and deficits, and continually improve forecasting precision.
Process mining for operational resilience
This technique uses timestamps generated at various stages along a process flow and instantly creates a visual and statistical representation of the reality of any process and be able to analyze outliers, and apply powerful statistical techniques to enable remediation and optimization efforts.
ESG document intelligence
Data sources required to effectively embed ESG into financial processes, including know your customer (KYC), trade finance, credit scoring, and investments, are many and varied. The ability to leverage unstructured data through document intelligence is critical. Currently, organizations rely on individuals to read sections of these documents, or search for relevant materials without a systematic way of categorizing and understanding the data.
AI for asset management
The role of data and analytics has become heightened and by using data science and machine learning platforms to usher in these changes, asset management firms are essentially killing three birds with one stone. One, they are changing the mindset of stakeholders throughout the firm — data is not to be leveraged in ad-hoc, siloed projects, but rather the transition should be one that is thoughtful and holistic.
Asset managers are paving the way for a new business model, one that has data woven into every business process, underpinning essential business operations and ensuring relevance in a changing world. Lastly, they are enacting a framework for organizations to profitably scale and double down on efficiency when it comes to data initiatives. This alignment of people, processes, and technology therefore becomes business critical for asset management firms.
