Article attributed to Thomas De Souza from Hitachi Vantara | 8 October 2021
By Thomas De Souza, CTO, Financial Services
Hitachi Vantara
Financial institutions find themselves in a fast-changing competitive landscape that is being driven by data and advanced algorithms, to survive and be successful, institutions must step back and take stock of their analytical capabilities and underlying data platforms to understand how they can increase their data agility.
As more transactions become digitalised, and depend on advanced technologies, such as machine learning, this will open up new possibilities for financial products and services, however, legacy and siloed systems may not be able to keep up.
Currently, many financial institutions store their data in disparate systems, making it difficult for them to get access and build on the data held within these systems. This impedes their ability to reshape and repurpose the data, and to tap on data analytics to detect patterns and trends, create intelligent product features and build next generation products.
Furthermore, when available data is stored in disconnected or loosely connected systems, any significant change that affects many of them can take months, if not years, to implement. As there is no common data environment, this increases the complexities and risks of executing such changes, which could result in data integrity issues and affect system reliability, increasing the likelihood of outages.
Building a Robust Foundation for Agile Data
Driving data agility will enable institutions to drive product innovation and high levels of automation within their lines of business and across their operation platforms. To start this journey, organisations need to first understand the current state of data environment and then develop an agile data strategy, capturing existing technologies, skills needs and organisational readiness and developing a plan for change which clearly articulates short and long-term return on investment priorities.
It’s important to establish a strong foundation to build data agility and the key to this is creating a data fabric that brings data into a common and shared data environment that can support a converged analytics capability that allows for different types of analytical, statistical and machine learning to be employed alongside each other.
Adopting a data fabric, organisations can ensure they have simple and consistent access to all relevant data, updated in real-time which can be embedded within their products and operational processes.
Equally importantly, with the collated and standardised data environment, businesses can more effectively deploy data analytics using advanced pattern detection, correlation identification, and use the insights to drive the development of new data driven digital products and services.
This will empower analytics teams to be able to use advanced analytical and machine learning methods to make better decisions and gain deeper business insights, as well as making data analytics tooling available to non-technical users, enabling more informed decisions across the organisation.
The data fabric will also provide several key data management capabilities that are vital to creating a sustainable and compliant data environment. Central to this is intelligent data management using AI natural language processing techniques to classify and scan data to ensure security, privacy, and regulatory compliance, which allows for data to be safely accessed across the organisation and in the public cloud.
Data Driven Innovation – the Future of Financial Services
Financial services organisations that can successfully make the transition to an agile data environment will be in a good position to increase their competitive advantage by using data and advanced analytics to move faster and innovate quicker than their competitors.
The wealth segment has historically been a high-touch personalised service. However, the demand for wealth services has risen and the needs of customers have changed with customers wanting a real-time view of their investment and advisory services on their mobile devices. Financial service organisations that are leading in this segment are aggregating data from their transactional system, the financial markets and institutional investors who manage these investments. They need to provide a seamless experience for their customers by using various advanced data analytics and machine learning methods to interact with the customer and adopt investment algorithms to allow investment management to be automated and support investment decisions in real-time.
One of the largest online financial services and payments platforms in Asia is using customer interaction, historical purchase records and transactions to determine credit rating in real time to extend credit on online purchases. They are using both transactional and non-transactional data in conjunction with artificial intelligence algorithms to make better lending decisions and adjust loan rates in real time, which has significantly reduced loan delinquency and increased lending profitability.
Financial services organisations that do not embrace an agile data strategy to drive product and service innovation will struggle to compete against other fast-moving competitors who are using data to create new products, changing the competitive landscape as they do so. By implementing an agile data infrastructure or data fabric, they can stay ahead of the curve, achieve innovations and improvements to not just remain relevant, but stand them in good stead for the future.
