This article was originally published in Analytics India Magazine.
From personalisation to customer-centric approaches, predictive risk management to defining product optimisation 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. It leads to an uptick of data scientists and data translators; who analyse 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 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, 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 from where most of the new roles will emerge.
Analytics India Magazine and UpGrad chalk out a few data analytics roles that will emerge in 2017 and what goes into their making:
1. Fraud Prevention Analyst
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. 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. 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.
2. Credit Risk Analyst
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
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 organisations 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.
4. Anti-Money Laundering (AML)
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 organisations.
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
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.
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 touch-points.
Apply analytics to understand the customer’s pain points and personalise the journey.
Use predictive analytics and ML technique to predict future behavior.
Rework the platform with the 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. And if you do not have the prerequisite skills or want to up-skill with specialized courses, then you can enroll in UpGrad’s Data Analytics! 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!