From personalization to customer-centricity, 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 space. According to a recent Mckinsey guide to surviving banking in 2017, “data is the center of every meaningful decision,” — leading 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.
And the banking industry’s dependence on quantitative analysts, popularly known as quants, has deepened further. With the roles becoming more widely spread across banking institutions, the future of banking will be defined by this new data talent that can combine skills and expertise across major aspects such as – big data, analytics, digital, risk mitigation and fraud prevention.
Will 2017 be a better year for data science? 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 and video and geospatial; data analysts will play a leading role in 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 cyber threat, global terror and compliance and risk management. These are the areas from where the new roles will emerge.
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Fraud Prevention Analyst
Must-have Skills: A good grounding in statistics, applied math and algorithms, expertise in Python, Java and knowledge of HBase is definitely 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 based system that has been deployed by many financial institutions, a case in point is storied company Mastercard. In Mastercard’s case, 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 spike in spending by leveraging customer’s account information, device, location, type of purchase, merchant etc. 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.
Credit Risk Analyst
Must-have Skills: Besides statistics, knowledge of BI tools, risk modelling framework
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 the face of new and tighter regulations that came 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. A large talent gap in India, in this area, has already been pointed out by a CRISIL report.
Data Science Translator in Banking
Must-have Skills: Besides the usual Data science skill-set, excellent communications, industry domain and trends and new technologies are a must-have.
Potential Employers: Mckinsey
Job Role: It’s a profile that is almost unheard of, so far, and has been birthed by one of the best financial institutions globally, Mckinsey. Mckinsey is defined as one of the leading analytics-driven organizations across the globe. They recently posted this 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 lifecycle management and have the core skill of converting data into BI and meaningful insights. Keeping the usual skill set aside, data translators would be having a client-facing role, probably acting as a bridge between the team and clients.
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 by collecting the right information for meeting regulatory obligations. This information should be made digestible and viewable 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, a global leader in digitally powered business process management and transformation offers advanced analytics tools in AML/KYC that also provide due diligence and a world class screening platform.
Customer Service Analytics
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 sizeable 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.
The use of data analytics in customer service encompasses four basic points across which customer journey specialists work:
- Collect data covering the customer journey including the varying touchpoints
- Apply analytics to understand the customer’s pain points and personalize the journey
- Using predictive analytics and ML technique to predict future behavior
- Reworking the platform with new data discovered
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 from. And if you do not have the prerequisite skills or want to upskill with specialized courses, then you can enroll in Data Analytics course provided by one of the leading edtech providers UpGrad, kick-started by UTV founder Ronnie Screwvala. The course is co-created by IIIT-B and has an eminent line-up of faculty alongside industry veterans who lend domain expertise. As part of the program, you get the opportunity to specialize in BFSI domain through course work and through an industry relevant Capstone project.