Building Data Analytics into A Core Function for Lenders

Evolve business strategies with future-ready data ecosystems and prepare teams for advanced analytics

Artificial Intelligence has undertaken mature roles in Finance. Creating reliable data banks is one of its main functions today. With this, we see an increase in data quantity and quality, leaving businesses with a simple-sounding, yet critical task to — 'make data work better'.

A well-rounded, data-led strategy requires software and hardware overhauls. Because of the high costs for this, solution providers need a proactive approach toward helping lenders maximize benefits from costly systems. Here, quite a few activities help Banks and FIs turn data analytics into a core function.

To begin, let's look at a challenge that data solution providers have solved with an innovative approach.

'Data Analytics as a Core Function' - Where Do the Challenges Lie for Banking?

Banks and Financial Institutions (FIs) have lesser freedom in choosing data solution providers. Some factors like stringent regulatory compliance and security for sensitive details are roadblocks to outsourcing data-related tasks.

Though, with web-based data management and computing, a few reputed companies have built innovative solutions to overcome these challenges. Customizable off-the-shelf products are being offered based on SaaS (Software as a Service) and Cloud modules for dependable upgrades to legacy systems.

These web-based systems put complex data ecosystems in place. But, that’s not the end of it. After solution deployment, a question remains – Can data solution partners offer more to build data analytics into a core function? To build on that, here's what your data solution partner should offer.

Training and Education Programs for Data-readiness

Banks and Financial Institutions (FIs) need to invest in analytics education programs. After recognizing data analytics as mission-critical, stakeholders need to fund efforts to build a community that cultivates data-centered culture — not just internally, but with others in the industry as well.

To improve the quality of data processes, a few members of Banks need to be proficient in understanding data dependency, functions of analytics dashboards, and report generation for a number of use cases.

Here, for successful data usage, solution partners should simplify enterprise-wide data delivery to play their part in time-and-function critical decision making. Exploring new markets swiftly is possible where simplified data is easily accessible to multiple teams.

Sometimes, data is required in real-time and should be easily shareable too.

Data solution partners solve a large chunk of the puzzle, but data literacy is pivotal for further improving these processes. Moving further, Banks will need trained teams to explore and evolve data utilities that can be shared with other Banks and FIs.

Majorly, data utilities will soon be blockchain-based. These boost cost-efficiency and quick development of data-centered systems. Many trends today show that using data utilities is the safest way to deal with data cleansing for higher-quality insights.

Professing a Shift in Work Culture to Accommodate Big Data

As big data sources evolve, quicker strategizing is inevitable. Analytics helps in adaptation to unseen scenarios, where real-time data saves the day. To tackle an increasing reliance on ever-evolving big data, a work culture centered around big data analytics readies teams to embrace proactiveness.

As of today, brick-and-mortar banks use big data to remodel customer segmentation and build solutions tailored to changing industries and their customers. Where basic customer segmentation doesn’t address pain points in real-time, big data helps to formulate individualized profiles and monitor them closely, mostly 24-7-365.

Moreover, credit card companies, retail banks, private wealth management advisories, venture funds, and institutional investment banks all need big data for new-age financial analytics. It helps in quick-ideating to build market-acquisition-and-retention services.

Build Solution Architecture that Helps Maximize Data Usability

Massive amounts of multi-structured data live in multiple disparate systems. With modernized solution architecture, data usability can be increased to improve data-driven –

  • Data Warehouses | Banks need teams that understand and make use of the democratization of analytics. Well-planned solution architecture helps with democratization – simplifying data to increase self-service – for teams to derive comprehensive insights.

  • Data Flow Automation | Insight generation for regulatory reports like IRAC, Ind AS, and IGAAP require data flow from systems for loan origination, credit monitoring, and risk-based supervision. Data flow automated using AI/ML-powered systems helps increase the pace for the generation of these reports.

  • Cloud Implementation | Real-time insights around borrower portfolios need interconnectivity between external and internal data sources. Cloud-based solutions help build highly flexible databases for the easy inclusion of new data sources. Cloud-based storage also makes data ecosystems highly scalable.