Transforming Financial Services with Big Data Analytics

Published by MBA Skool Team, Published on November 05, 2015

Statistics 1

1. 70 % of the banking executives worldwide consider customer-centricity to be of vital importance.

2. More than 60% of financial institutions across the whole of North America believe that “Big data analytics” can offer a significant competitive advantage.

3. 90% of the people believe that successful “Big data” initiatives are going to decide the winners in the future.

Statistics 2

1. Only 37% of the banks across financial industry have hands-on experience with live Big Data implementations while majority of them are still in the “pilot testing & experimenting” phase.

2. Only 50% of the total numbers of banks make efficient utilisation of the external customer data such as those generated through social media activities and online behaviour.

3. Only 29% of the banks analyse “Customer’s share of the wallet”, which is one of the major indicators of the customer’s loyalty towards a bank.

The above sets of statistics are contradicting in nature as the first represents the holistic view of the financial services industry towards the paradigm shift that “Big Data analytics” is capable of bringing while the second is reflective of the nonchalant attitude of the industry in implementing the “Big data” integrated/facilitated systems.

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In other words, the industry acknowledges the massive opportunity that “Big data analytics” presents but is yet to figure out how to use the structured & unstructured, old & new, internal & external data that it has access to in order to create a sustainable competitive advantage.

At this point it is imperative that the need for “Big Data analytics” in the financial services industry be discussed. In an era, where possession of a cutting-edge technology is no longer a differentiator due to its easy replicability; enterprises are increasingly realising that it is not the sophisticated technologies but their earnest packaging into a “service” which have the potential to attract and retain customers. “Customer centricity” is thus of paramount importance across all industries. And in operationalizing this strategic focus, “Big data analytics” has a major role to play. From seemingly ordinary “data” collected from the customers through the “Know your Customer (KYC)” forms such as Name, Gender, Age, Birth Date, Profession, Dependants etc. analytical tools can mine for useful “information” which not only renders greater insights about customer preferences but also subsequently helps in designing products (loans, insurance schemes etc.) which serve their needs better.

The usage of data analytics also caters to the underlying risk that the financial services industry has been inherently suffering from: the risk of a “bad debt”. Inaccurate assessment or monitoring of a potential customer’s risk profile and his/her transaction patterns can imply an impending default of charge-off. With data analytics, the process of assessment and monitoring becomes a lot easier since additional insights on the customer such as his/her credit history, health history, active accounts with other financial institutions, family background, rental history, purchase returns and even mailing lists can be obtained and assessed. Thus, with an increased knowledge about the customer the possibilities of charge-offs are exponentially reduced.

While the above discussed reasons are the primary ones, it is also noteworthy that there are a host of other functions/processes in which “Big data analytics” can be used with great efficiency such as portfolio management, balance sheet and performance evaluations, fraud detection and other risk management strategies.

The challenges that the acceptance and implementation of “Big Data analytics” faces in the financial services sector are many. To begin with, most of the customer data resides in silos across the entire value chain of a financial organisation. Simply put, the traditional systems of data collection and storage do not provide for centralised data integration which can be accessed across the board. Case in point being Deutsche Bank, which embarked upon a Big Data project to analyse the unstructured data they possessed. However, the bank faced a lot of difficulties extracting data from the traditional legacy systems. Another matter of contention that is invariably raised is the “Privacy issue”. How ethical or unethical is it to analyse prima facie unrelated pieces of data and extract important sensitive information about the customers is a question that generates reluctance towards the acceptance of “Big Data Analytics”. Besides which how secure such sensitive customer information is, in the hands of the financial institutions is also worth a thought. Any robbery or security breach could lead to highly classified customer information ending up in the wrong hands.

Yet another hindrance is the lack of the skill sets required to efficiently harness the opportunities that Data analytics offers. Data management skills which include statistical, programming and mathematical skills are imperative for Data analytics to be useful. And in most financial institutions employing the traditional co-relational analytics, employees are found to be lacking these skills. Research suggests that three quarters of the total number of banks lack the adequate knowledge to gain value from Big Data. Finally, though financial institutions emphasize greatly on Customer Relationship management (CRM) and as written above; consider “Big Data Analytics” to be the torchbearer for this strategic focus, the fact remains that most enterprises treat it as just “another IT Project”.

What emerges significantly from the above discussion is the need for a change both at the operational level and at the cognizance level. Any usual IT Project would be steered by the three constraints of sustainability, stability and scalability. However, in addition to these implementing Big Data Analytics would necessitate agility in discovering data, ability to mine data and the acumen to put the filtered information into efficient use. The perceived value that banks derive from every dollar spent on Big Data Analytics is merely 55 cents. This figure echoes the need for increasing the efficient usability of data analytics. And this in turn indicates the need of extensive assessment and training programmes across financial institutions which would not only identify the data analytics oriented skill gaps that employees have but also develop those skills in them.

An increase in the customer wallet share, market share and customer retention: the three major goals of every financial services institution can be attained by leveraging the humungous customer data they possess by maximising the usage of Big Data Analytics. On a larger canvas, it indicates a shift from data collection to information analysis or in other words from an external locus of control to an internal locus of control.

This article has been authored by Purbasa Patnaik & Anurag Soni from Xavier Institute of Management


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