5 New Data Analytics Roles that will Define the Future of Banking

This article was originally published in Analytics India Magazine.
From personalization to customer-centric approaches, predictive risk management to defining product optimization across various channels. The banking sector has given rise to a new breed of talent across the data analytics career space. According to a recent Mckinsey guide to surviving banking in 2017, Data is the center of every meaningful decision. It leads to an uptick of data scientists and data translators; who analyze large volumes of data and convert them into a new product or drive product enhancement for the end-user.

Further, the banking industry’s dependence on quantitative analysts, popularly known as ‘quants,’ has deepened. With the roles becoming more widely spread across banking institutions, this new data talent will define the future of banking. It can combine skills and expertise across major aspects of – big data, analytics, digital, risk mitigation and fraud prevention.

Will data science have a better future? Definitely. And there will be a spurt of diverse roles across the financial sector.  As banks grapple with large volumes of data of all sorts – social, text, video and geospatial. Data analysts will play a leading role in the lending sense to data and deriving customer insights such as behavior predictions and delving into customer sentiment.

The key areas where data analytics is applied are customer-centricity, cost containment, combating the cyber threat, global terror, and compliance and risk management. These are the areas where most of the new roles will emerge.

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Analytics India Magazine and UpGrad chalk out a few data analytics roles that will emerge and what goes into their making:

1. Fraud Prevention Analyst

5 New Data Analytics Roles that will Define the Future of Banking
Must-Have Skills: A good grounding in statistics, applied mathematics and algorithms. Expertise in Python, Java and knowledge of HBase is a must have.
Potential Employers: EY, Genpact, Deloitte, HPE Infosys, TCS
Job Role: Predictive analytics was the gold standard in fraud mitigation strategy but what’s come into focus recently is dynamic machine-learning (ML) based system. The scope of Data Analytics is only getting higher. Many financial institutions have deployed it. A case in point is a storied company, Mastercard.

At Mastercard, the use of sophisticated algorithms for intelligent analysis not only provides more accuracy but also real-time information that brings down false declines, and increases approvals, for genuine transactions.

Mastercard’s Decision Intelligence solution, rolled out last year, probes an existing account overtime and detects anomalies such as a spike in spending by leveraging customer’s account information, device, location, type of purchase, merchant etc.

The role of an Analyst in banking industry includes providing consultation to customers, planning and executing day to day projects, investing their time in searching if the customer is credit worthy or not and preparing financial and market analysis. Through unique algorithms, banks deploy ML techniques to rule out anomalies in a customer’s spending pattern and leverage artificial intelligence techniques to improve the overall customer experience.

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2. Credit Risk Analyst

5 New Data Analytics Roles that will Define the Future of Banking UpGrad Blog
Must-Have Skills: Besides Statistics, knowledge of Business Intelligence (BI) tools and Risk Modelling Framework are also important.
Potential Employers: Accenture, Genpact, SAS, Mckinsey, TCS
Job Role: Credit risk management deploys preventive measures and relies heavily on preventive analytics to enable banks to mitigate the likelihood of defaults. Post the 2008 mortgage crisis, banks have strengthened their credit risk portfolio in light of new and tighter regulations that have come into force.

The job of a credit risk analyst entails making sound business decisions through advanced credit analytics. This is where prescriptive and predictive analytics comes into play. CRISIL report already pointed out a large talent gap in India, in this area.

3. Data Science Translator in Banking

5 New Data Analytics Roles that will Define the Future of Banking UpGrad Blog
Must-Have Skills: Besides the usual Data Science skill-set, excellent communication; an understanding of industry domain, trends and new technologies is a must-have.
Potential Employers: Mckinsey
Job Role: It’s a profile that is almost unheard of, so far, and one of the best financial institutions globally, Mckinsey started it. Mckinsey is defined as one of the leading analytics-driven organizations across the globe. They recently posted a new job title for their analytics team. According to Mckinsey, a Data Science translator is one who has a solid base of advanced analytics tools such as Tableau, Hive, Hadoop, Spotfire and a grounding of programming languages like R, Python and SAS.

Besides a demonstrated ease of working with huge data sets, a Data Science Translator is proficient in network analytics, customer life-cycle management and has the core skill of converting data into BI and meaningful insights. Data translators would also be having a client-facing role, probably acting as a bridge between the team and clients.

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4. Anti-Money Laundering (AML)

5 New Data Analytics Roles that will Define the Future of Banking UpGrad Blog
Must-Have Skills: Knowledge of advanced statistical methods, proven experience in financial data analysis and a familiarity with AML issues.
Potential Employers: Infosys, Genpact
Job Role: Data Analytics can play a huge role in combating global terror by stemming the flow of funds to terrorist and criminal organizations.

AML has grown into a service stream with financial institutions deploying AML-KYC solutions and tapping varying sources of data for leads. The first step in tackling money laundering is to collect the right information for meeting regulatory obligations. This information should be made digestible and visible through the right set of BI tools.

To sharpen the process further, another layer should be added to the database to extract the right kind of data which will help in finding the right patterns of data. Genpact, is a global leader in digitally-powered business process management and transformation. They offer advanced analytics tools in AML/KYC that also provide due diligence and a world-class screening platform.

5. Customer Service Analytics

5 New Data Analytics Roles that will Define the Future of Banking UpGrad Blog
Must-Have Skills: Proven knowledge of SAS, R, and experience in building marketing analytical models, problem-solving skills and knowledge of techniques such as regression and market basket analysis.
Potential Employers: IBM, Salesforce.com, Oracle, TCS
Job Role: Want to create customer-centric products by leveraging big data? Want great insights on what might click with the new-age customers before making a sizable investment? You will have to tap into reams of data before uncovering valuable insights such as customer’s pain points and spending behavior.

From sentiment analysis to customer-focused products, banks are increasingly driving engaging experiences by building excellent analytical capabilities through Customer Journey Analytics solutions. The analytics platform identifies key opportunities and can also predict future behavior.

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The use of data analytics in customer service encompasses four basic points across which customer journey specialists work:

  1. Collect data covering the customer journey, including the varying touch-points.
  2. Apply analytics to understand the customer’s pain points and personalise the journey.
  3. Use predictive analytics and ML technique to predict future behavior.
  4. Rework the platform with the new data discovered.

Wrapping up!

Future of Data Analytics

Well, as we discussed above the scope of Data analytics, which is huge in the near future. The role of Data Analyst in banking is to gather analytical reports from the insights and help the institution and its employees make a better decision for the future. Moreover, the role of Analytics in the banking industry is crucial as the growth of the company is based on the strategies you make to get the desired results. 

Another field of analytics includes Data Science translator as discussed above. The Data Science scope in India is valuably increasing as Banking sectors earlier used to avoid Big Data and Data science to avoid fraudulent. Now that the industry is emerging immensely, the scope of Data Analytics in India is increasing in direct proportions. The future of Data analytics depends on Data Science as it deals with both the unstructured and structured data. Then comes cleansing, mining and analyzing data to an extent where you can come to the beneficiary measures which should be initiated for the growth of the company. All of this comes under Data Science.

If you are keen on leading data-led innovations in the banking sector which is primed for exponential growth in 2017, there are a plethora of roles to choose. And if you do not have the prerequisite skills or want to up-skill with specialized courses, then you can enroll in UpGrad’s PG Certification in Data science! The course is provided in partnership with IIIT-B. As part of the program, you get the opportunity to specialise in BFSI domain through coursework and through an industry-relevant Capstone project. Check it out now!

How does Data Analytics help Banking?

Data analytics bolsters a bank's marketing capabilities. Risk, Compliance, Fraud, NPA monitoring, and Calculating Value at Risk are just a few of the functional areas that may benefit tremendously from analytics to guarantee optimal performance and to make critical choices when speed is critical. It may give useful suggestions to top management at every step of the customer lifecycle. Data analytics provides a thorough view of the normal banking client life cycle and its different stages, beginning with onboarding. Data analytics may assist banks in differentiating themselves and being competitive in the future.

What is fraud prevention analytics?

The application of big data analysis tools to combat online financial fraud is known as fraud analytics. It assists financial institutions in forecasting future fraudulent conduct as well as detecting and decreasing fraudulent behaviour in real time. Financial institutions amass vast quantities of behavioural, transactional, and device data. Financial fraud can be prevented and detected by analysing this data by a fraud detection system and/or a fraud investigation team. A machine learning-based fraud analytics system may utilise a variety of big data analysis approaches to prevent financial fraud if it has good data.

What is AML?

Anti-money laundering, abbreviated as AML, refers to the efforts that financial institutions engage in to ensure that they are in compliance with regulatory obligations to actively monitor and report suspicious activity. In the case of AML, data analytics entails comparing the customer's predicted behaviour to their actual behaviour on a continual and automated basis. This will aid in determining whether or not a consumer is likely to conduct a financial crime. Money laundering may be detected early by using predictive analytics.

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  1. The foundation of any analytics predictive or prescriptive is good quality data and a linear model that will help all the end users of these skillsets with accurate timely decisions. That said, analytics tools have definitely come of age and are gradually evolving into expert systems. The big question for local banks is how many are harnessing above 30% of data outside their core banking systems for the key uses of predicting fraud, calculating credit risk, portaying accurate customer sentiment even when such sentiment is in slang or local dialects? Nevertheless a very good read and indication of the growing merger of the analytical skill set with other skills e.g. programming, statistics, math, customer experience.

  2. Thanks Dennis – you’ve made some very interesting points and observations. Do continue reading our Blog and share any other inputs you have on what more you would like to read!

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