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Data Analytics: To scale or to departmentalise?

by Alix Randriana

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“Data Analytics” has become a popular catch-phrase in banking in recent years. However, the phrase has been so repeated across industries that this wide-ranging function could be that carried out by, just to name a few, an IT professional, a marketeer or a mathematician. Whilst it’s no longer the new kid on the block, it is worthwhile considering the value this function brings in its implementation across organization. Particularly, should data analytics be departmentalised like Accounting or Human Resources? Or, should it be integrated into every department to add value in different ways?

Alix Randriana, Senior Consultant at Space Executive finds that there has not been a consistent approach across businesses. “There are numerous ways data analysts can add value to each team and each function. We have seen how some multinational companies apply the concept of “compartmentalization” and group their analysts in one division. We have also seen how other organisations attempt to integrate data analysts at scale across each of their teams. While there is no right answer, the inclusion of data analysts should be a dynamic process for companies to constantly assess their performance and progress,” Alix comments. 

McKinsey &Company suggested in a section on “Implementing advanced analytics at scale” in their white paper that banks should adopt advanced analytics throughout their businesses and not only in specific areas. [1] This can create a sustainable competitive advantage. What this really means is having data analysts working for each and every department in an organization. Without such integration, data stays in silos serving the needs of limited groups. This leads to the question of the relevance of data analysts across a wide array of banking functions.

Client-centric use of data analytics first comes to mind. Banks are well-advanced in utilising data analytics for such purposes. Sam Kumar, Global Head of Analytics at Standard Chartered Bank suggested, in an interview with the BBC, “Data offers the intelligence to make sure what the client is being offered by the bank has value and relevance to their choices.”[2] Naturally, data analysts mining client-centric data parameters need to be integrated with business development to enhance the study of client consumption characteristics. Lloyds Banking group said it is working with Google Big Query and Data Flow to analyse client behavior and their requirements in order to deliver solutions real-time.[3] RBS also uses data to better connect with clients, sending messages on their birthday or having staff call a client who can borrow at a lower rate.[4] These are ongoing processes as the amount of data collected increases and evolves over time when trends change. Over time, data analysts become entrenched into this business function rather than being just a project-based resource to be seconded from a common pool.

Other non-client centric use of data analytics should not be neglected. “Credit risk is a crucial business function in banks where interest income is their core business,” says Alix.  Moody’s Analytics has introduced tools targeted at using data analytics in assessing credit risk.[5] Data analysts are again increasingly relevant to other non-client centric core business functions. It also goes to show that data collected from clients can have multiple uses. They should not only be used to maintain and build relationships with the clients but they can be used to the benefit of the bank. The integration of data analytics in business development and credit risk work hand-in-hand to appropriately and efficiently disburse the greatest loan to the client at the least risk to the bank.

J.P. Morgan CEO Jamie Dimon once said in an interview with Bloomberg, “Silicon Valley wants to take on this business. They are using big data for the credit side of lending. They can underwrite it quicker. For example, they might lend to one of our customers who’s got a $200,000 J.P. Morgan Chase loan, and this person wants to get another $20,000 for a new truck or a piece of equipment. He goes with them because he gets it in 15 minutes.” J.P. Morgan Chase has since set up a 200 strong Intelligent Solutions unit that Dimon thinks can compete with any firm on Silicon Valley.[6]

The banking industry, given its nature, is often highly regulated. Banks can therefore benefit by applying data analytics in their compliance with regulation on anti-money laundering and countering the financing of terrorism. In Singapore, the Monetary Authority of Singapore and Singapore Police Force have put together an initiative on share data analytics to combat financial crime.[7] The implementation of this initiative will include data collection from various banks and the ultimate sharing of such information with the authorities.

"Applying data analytics to this data set has enabled us to identify suspicious fund flow networks, and focus our supervisory attention on networks of higher-risk accounts, entities or activities," said Ms Ho Hern Shin, MAS' assistant managing director, banking and insurance group at a financial crime seminar organized by the Association of Banks in Singapore.[8] This is an intensive task requiring coordination from all departments of the bank, fueling the need for an organization level implementation of data analytics.

Data analytics has grown into an essential part of core banking functions that it can no longer be a standalone function. This has created a greater demand for data analysts especially those with skill sets beyond computational analytics. “In line with the integration of data analysts into each department, experience in various business functions of banking such as compliance becomes a much valued attribute as the data analyst can seamlessly integrate and contribute operational departments,” adds Alix.

Please contact Alix at arandriana@space-exec.com for more market incites, discuss your career goals or hiring needs.

[1] McKinsey&Company, “FIG- Banking, Advanced analytics in banking – a sneak peek”, October 2018.

[2] “Banking on innovation - Demystifying Big Data in banking”, BBC, http://www.bbc.com/storyworks/banking-on-innovation/bigdata.

[3] “Data analytics drives retail banking”, 10 May 2017, Richard Hartung, http://www.theasianbanker.com/updates-and-articles/data-analytics-drives-retail-banking.

[4] “Data analytics drives retail banking”, 10 May 2017, Richard Hartung, http://www.theasianbanker.com/updates-and-articles/data-analytics-drives-retail-banking.

[5] “Data Analytics and the Future of Credit Risk Management”, August 2017, Moody’s Analytics, https://www.moodysanalytics.com/webinars-on-demand/2017/data-analytics-and-the-future-of-credit-risk-management .

[6] “Data analytics drives retail banking”, 10 May 2017, Richard Hartung, http://www.theasianbanker.com/updates-and-articles/data-analytics-drives-retail-banking.

[7] “Financial industry shares good data analytics use cases to fight financial crime”, 29 Nov 2018, AML/CFT Industry Partnership (ACIP), http://www.mas.gov.sg/News-and-Publications/Media-Releases/2018/Financial-industry-shares-good-data-analytics-use-cases-to-fight-financial-crime.aspx.

[8] “Fighting financial crime with data analytics”, 19 July 2018, The Straits Times, https://www.straitstimes.com/business/banking/fighting-financial-crime-with-data-analytics.