Digital transformation is a buzz word and new age technologies like artificial intelligence, machine learning and data science are disrupting the banking industry similar to that of every other industry in place. Data is the most important resource of the new age and efficient management of it through APIs and data science could do a world of good for the wealth management and banking industry. In an exclusive interview with DataQuest, Faisal Husain, Co-founder and CEO, Synechron, talks about how exactly Data Science platforms are being used for wealth management and banking industry.
Significance of APIs in the Banking Industry
As the banking industry embraces Big Data and advanced analytics to enhance customer experience, data sharing APIs have started receiving enormous attention as a means of acquiring data quickly for authentication and other purposes. Though data sharing APIs are widely adopted in Europe and the U.K., India started its journey through key initiatives such as Aadhaar, e-KYC, e-Sign, privacy-protected data sharing and the Unified Payments Interface (UPI).
These APIs provide access to various financial services to anyone with a mobile phone and reduce costs of authentication for financial institutions. In the near to medium term future, APIs are expected to mature, and India may move towards an open banking system wherein data is available for the FinTech industry or to anyone interested in creating products and services based on data analytics.
Intertwining Data Science, AI, and ML Tools Produce Cost-effective Research for Wealth Management
Wealth Management, as a market is becoming increasingly competitive. Businesses must continually improve to stay ahead and the best way to do that is offer services customized to suit the specific needs of clients. With advancements in data collection, storage, retrieval and processing infrastructure, enterprises possess an enormous amount of data that could be mined to get valuable insights.
Data Science, AI and ML tools provide wide ranging algorithms to mine data and extract patterns which could later be used to offer personalized services and enhance client satisfaction. The research insights provided by these algorithms are at par with any skilled asset or wealth management professional and provides consistent output. Specifically, with the advances in big data and analytical algorithms, Robo-advisors are taking over the role of traditional wealth managers thereby reducing the cost of provisioning such services.
Real-time Portfolio Management using AI & ML Algorithms to Detect Stock Market Movements
Wealth management firms adopt AI and ML algorithms to detect stock market movements on a real-time basis and rebalance their portfolios to achieve a pre-defined financial objective. The algorithms along with providing indications on stock market movements provide ample opportunity for wealth managers to simulate what-if analysis, identify risk and mitigate them and conduct position analytics.
As AI and ML algorithm takes over the role of monitoring of portfolio, analyzing signals coming out of varieties of sources and make most of the decision making; wealth managers have ample time to connect with their clients and manage several portfolios of all sizes.
Cybersecurity Landscape in India and Wealth Management
With the increasing adoption of digital technologies and processes by the BFSI sector, the cyber threat landscape is also expanding. India comes in the category of an ‘Aspirant’ in Cybersecurity Capability Assessment and is stepping up efforts to reach a leading position in the years to come.
The government, industry and academia are indeed collaborating to create appropriate policy environment, law enforcement, human capital development, research, innovation and technology development, infrastructure building and financing to scale up to a cybersecurity standard that is consistent with other developed markets. These initiatives have already made the cyberspace more secure than ever and we expect it to further evolve into a highly conducive environment that enables safer handling of data in financial services, including wealth management.
How Data Science Platforms are being used to Identify Correlation and Causation across Wide Range of Data Sources
Back in 1973, Burton Malkiel, in his famous book “A Random Walk down Wall Street”, implied that asset prices consistently exhibit signs of ‘Random Walk’. This is why financial forecasting has always been a tough ask to achieve even by professionals in this industry. However, in this world understanding correlation and causation directly result in better forecasting and predictive models. Correlation looks at linear relationships across data points while causation would tell us that one event, in fact, caused another to occur.
At Synechron, Data Science teams have worked to create an end-to-end data ingestion and preprocessing architecture that is able to process large data volumes and hold inbuilt models from the Pearson Correlation Coefficient to Granger Causality. This has helped create better forecasting models. Synechron’s Syn-AI Platform, a result of Financial Innovation Labs (FinLabs) is an example of how Data Science has worked extensively to identify and understand correlation and causation between such events from multiple different data points.