Overcoming the data challenges in the financial sector
Challenge identifier: SC4-2018
Finance has been slower than others in embracing the opportunities for change presented by technological advances. However according to Capgemini it is now catching up: business including banks are getting better at adopting open models through shared data; blockchain technology is maturing; Virtual Reality (VR) is offering the promise of delivering branch-free banking for example. New challenger financial services businesses, fintechs and banks are being launched, and consumers are being empowered by greater access and control to their own financial data. At the same time there are new risks for financial and insurance services through increased possibilities for fraud and cyberattacks.
Data analytics is being used to optimise trading and investment decisions. New regulations and reporting requirements such as the GDPR and MiFID ii will also mean that there is a renewed emphasis on privacy and better use of data.
We are particularly interested in solutions that leverage closed and shared data in the following areas:
- New banking and insurance products – Open banking data in the UK, for example, is beginning to power the development of new products and services for consumers and institutions. These include competing banking products but also apps and services which use data to provide insight into budgets for individuals and provide data driven wealth management and investment products and services for high-net worth and institutions.
- Blending insurance and financial services – Banks are now also selling insurance services to their customers and similarly insurance operators are starting to offer banking services. The operators can have advantages when the study of typical patterns in one domain gives insights on the other.
- Detecting and combating fraud – Fraud identification and prevention is sought by all the financial institutions but there are barriers to adoption of new solutions in the form of tight regulation and organisational intransigence. This creates an opportunity for new data driven approaches for detecting and combating fraud including the use of location information and periodic notification when an anomaly occurs in the data.
Transparency of algorithmic investment – Decisions in banking are both sophisticated and obscure. There is no real means to measure how effective they are. Independent verification of investment and trading algorithms by third parties could be beneficial for regulation authorities, but also for financial institutions that want to change their operating paradigm to provide the end-users with more transparency on their operations.
Examples of data include but are not limited to:
- Financial market information
- Consumer and commercial transaction data
- Social media data
- Location data
Examples of outcomes include but are not limited to:
- New apps and services
- New prediction algorithms
- New intermediary technologies to integrate data sources
- New tools and business processes to help decision making, including those making algorithms more transparent and accountable, registries and distributed ledger applications
- New forms of hardware
- Applications must include details on how these outcomes will be tested and evaluated during the six-months acceleration programme.
Participants will need to demonstrate how their solution:
- Improves fraud detection;
- Improves financial/insurance products
- Increases financial transparency