The financial services industry (FSI) includes a diverse group of sub-verticals including banking, capital markets, and insurance. The industry is somewhat unique because it is an important provider to every other industry vertical, and as such it sits at the crossroads of digitization of experiences across a broad and diverse set of other industries. In fact, most financial products are a means to an end—whether that is the purchase of goods in a supply chain, taking a ride-share, protecting your investment in your house against storms, or saving for retirement. In a digitally connected world, financial services products are embedded with other experiences.
While financial services companies have always been heavy users of technology and highly networked with each other, the exponential increase in the demand for connectivity is disrupting the traditional “fortress mentality,” enabling new growth opportunities, and creating new business models. Cloud and AI adoption is well underway but it frequently lags other industries, due to regulation, risk averseness and legacy technology/data estates.
Our vision for the industry is embedded finance in a collaborative industry cloud—a finance industry that is no longer discrete and self-contained, but provides an ethical, intelligent set of services that embed within the experiences, business processes and marketplaces of other verticals. These services have the intelligence to adapt to the individual needs and context of each embedding, and intelligently adapt to the circumstances of each individual user/client. Instead of seeing finance as just another sector that will undergo its own digital transformation, we instead believe that embeddable digital financial services will allow other verticals, experiences and marketplaces to become finance-enabled. These services will operate on a multi-tenant cloud where the industry collaborates and overcomes the historic fragmentation and duplication via trusted access to data and functions across tenants.
In our vision, the financial services industry will be 10x more connected—both between industry participants and between FSI and organizations in other industries—and 10x more data-intensive in the insights and intelligence it operates on. However, the trusted collaborative industry cloud platform and cross-tenant data access will enable it to achieve this with 50% reduction in middle and back-office processing and IT expense. In short, the collaborative cloud will allow the financial services industry to be much more efficient, digitized and intelligent, and embedded within a multitude of experiences across all industries.
We have a range of research efforts to pursue this vision:
Project Titan is focused on novel methods for end-to-end protection of sensitive data across Microsoft’s three clouds. Large financial services customers and other highly regulated customers are keen to move significant data estates to the cloud and create intelligent applications around them but they are also concerned about the security of their most sensitive data elements and their usage. The mission of the FSI team in Microsoft Research is to enable customers to apply and enforce consistent protections on data across Azure Data engines, M365 and D365.
The FSI-grade Inter-tenant Information Barrier SDK project seeks to advance the technologies necessary to implement the trusted information barriers that allow services such as storage, data, and event messaging to safely operate cross-tenant. While FSI organizations implement strong controls at the perimeter of their organizations, their business needs are evolving to an increased need to become much more connected to each other and to other industries. This research seeks to remove significant cost and complexity from the current interconnections and create a network effect that pulls industry participants to our cloud in order to interact with their counterparties.
Our research on responsible and explainable AI is motivated by the industries increase in the deployment of AI to inform decisions and the growth in intelligent business processes required for embedded banking. Financial institutions and regulators are appropriately focused on how to responsibly deploy AI for consequential decisions such as lending decisions and capital holdings. We are working with these organizations, as well as the FairLearn and InterpretML toolkit teams, on the topic of how to enable responsible use of AI in a manner consistent and compliant with appropriate regulations.
Natural language understanding (NLU) research relate to the continually pursuit of alpha-seeking customers to identify ways to derive alpha from superior understand the context of investments. In our embedded banking vision, investment decisions are deeply rooted in understanding of the investment context and client objectives (e.g. ESG). Recent advances in natural language processing (NLP) by Microsoft researchers and others are opening up new possibilities in NLP/NLU. We seek to package NLP research efforts into an accessible solution for FSI and capital markets customers.
QLib is an AI-oriented quantitative investment platform which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment. It contains the full ML pipeline of data processing, model training, back-testing—and covers the entire chain of quantitative investment: alpha seeking, risk modeling, portfolio optimization, and order execution. We aim to establish QLib as a viable quant research framework in capital markets, and a platform on which we can introduce further research in AI models for capital markets. In doing so, we will support quant use cases in ESG sustainability for capital markets.