A data-rich industry 

Big data: How this asset can be a liability

The volume of data generated and held by companies has grown tremendously, over the last few years. With advancements in big data, analytics, and AI, the value of this data has also increased exponentially. As the banking industry is data rich, it is subjected to strict regulations and penal controls. To hold and archive this data securely is a significant liability for banking institutions.

Terabytes of data

Rising challenge of fintech unicorns

Many fintech unicorns (data-driven financial services) have emerged in the last decade. The technological innovations that they have introduced enable cheap and easy access to finance for a larger population. Lenient regulations, advanced technology, wider internet coverage, and smarter mobiles facilitated the growth of these fintech companies.

Today, the digital banking space is buzzing with advanced technology driven by social media, mobile computing, analytics (big data), cloud computing, and so on. Neobanks, payment apps, and Bigtech have emerged as incumbent components along with traditional banks in the financial ecosystem. Terabytes of real-time data are now available to offer deep insights into consumer behavior, predict customer preferences, and drive hyper personalization in banks.

A New direction

The way forward for regulators and banks 

Previously, regulations (pre-2017) did not envisage a wide scale secure internet connectivity and cloud infrastructure. EU (PSD2-2018) and Australia (CDR-2020) have recently introduced new regulations that take a modern view of technical advancements and promote shared data infrastructure in the banking and financial industry. Many other countries are also updating their regulations to allow banking and financial companies to share and utilize data.

In retrospect, banks have a significant advantage of exclusive possession of data. With new modern regulatory indicators, it is time to convert this advantage into winning solutions.

The following steps are required to use data effectively:

• Breaking the silos and unifying data management on cloud with entitlement-based access across internal organization
• Using cloud for data storage, to –

1. replace capital expenditure with operational expenditure
2. reduce overall cost—although initial migration may appear expensive the long-term ROI is significant—as it frees up IT personnel’s time to manage physical infrastructure
3. reduce data backup costs significantly and enable real-time backup availability
4. deploy AI-supported analytics tools within the organizational technology ecosystem

• Build data analytics capabilities by –

1. upskilling existing staff or hiring new staff with data analytical skills
2. engaging with vendors for the right skills in analytics and insights

• Enrich internal data with external data sources to create viable research products. The external data sources can be –

1. Open-source third-party data, for example, population statistics, demographic and economic data collected by the government
2. Private sources like social media players (Google, Apple, Facebook, Amazon, and the like) or local telecom companies which also offer rich data sets, such as geographic location data generated by telecom companies, or user browsing patterns.  

At face value, the data held and continually generated by banks, appears to be of little value in its raw state. However, if it is structured, formatted, and analyzed, it can offer significant tangible and intangible value. Today, as banks face major headwinds on traditional margins, monetizing data will alleviate some of the challenges.