We recently caught up with Mr. Rajesh Shewani, who heads Technology and Solutions at Teradata India (Leader in Data & Analytics). He spoke about how companies could make better decisions, fight fraud, improve operations & customer experience by using Analytics, AI & Machine Learning to mine hidden insights, patterns and unknown correlations from large chunks of data.
1. Which business areas can analytics impact the most?
Analytics impacts business areas across the enterprise – In the front office, middle office and the back office. Organizations need to take an integrated view which allows them to understand the relations in data across divisions, LOBs, functions and the impact that analytics creates gets amplified further and relevant insights generated add business value.
Some of the key areas are in Customer Experience where there could be a direct impact on revenue, brand and perception in the market, and in the area of Operational Excellence, where there is a direct impact on costs, processes, risk and compliance among others.
With respect to Finance Industry, Fraud is another area of focus. Fraud prevention and detection amount to high stakes business warfare. In the past five years, Indian Banking sector has lost as much as Rs.27,000 crore cumulatively due to fraudulent transactions. It is a known fact that no bank is immune to a Fraud loss in current times. Apart from the hard costs associated with Fraud, brand damage is one dire consequence an organization has to face.
A changing environment has offered fraudsters some tactical advantages and stiffened the challenge. Massive, rapidly accumulating volumes of uniquely structured data – much of it gathered from new opportunities to interact with customers – have opened new ground for fraudsters to exploit, even as the sheer volume has made it easier for them to hide.
Conventional approaches to fraud detection and remediation are necessary but they remain effective to a point, as conventional tools simply cannot effectively and economically process what is known as big data.
Big data analytics empowers companies to connect the dots and trace the movement of fraudsters in ways they have never done before – and at extraordinary speeds.
The data available with the banks can be easily analyzed to find out if there is unusual activity in currency flow at an end point or the person using the currency, no matter whether it is happening at an individual or regional level. Mathematical models and algorithms would indicate the approximate location of stashed money after analytics is run through the tools. “Big data analytics,” can enable companies to deploy and integrate rich and new data types to produce new and more sophisticated analyses against the fraudsters and continuously improving the loop of legacy approaches to the war on fraud. This wasn’t possible earlier with conventional data-processing methods. The tools process the Big Data to uncover unknown correlations and hidden patterns. Financial institutions and banks can leverage the meaningful trends and tips to scrutinise suspicious transactions, improve detection and surveillance, and predict and prevent financial fraud. In test cases, these analytics are devastatingly effective at exposing not just the fraudsters themselves, but their networks and the people, places and processes they touch or will touch.
2. How has the use of analytics changed in the recent times in India?
Use of analytics in India is still evolving and varies across industries and within industries it varies among organizations based on their maturity levels. Scale plays an important role in India, so whatever analytic technique that gets applied has to be supported for a volume that may not necessarily be done in other parts of the world.
The key aspect is around the quality of data, organizations have realized this issue and are using analytics projects to address quality issues as part of their core operational processes.
Lastly, the types of data we’re dealing with have also evolved, organizations are looking to exploit data sources such as text, emails, social, video, machine logs, weather and more, and combine this with internal structured data to uncover insights. We have moving to an era of Predictive and Prescriptive analytics.
3. How do you view the Indian payments & merchants ecosystem from a data perspective?
With the advent of digital payments and the ecosystem around it, there is a huge potential to leverage this to understand behavior – both of customers as well as that of merchants. The ability to understand what customer preferences are in terms of what they buy, how often and the value can be of immense importance to marketers when creating a strategy around all the Ps of marketing. Merchant ecosystem behavior analytics helps understand areas to service customers better as well potential risk and frauds.
4. Can you share some best practice examples of using data & analytics?
There can be hundreds of use cases across functions where data & analytics can be leveraged effectively, some of the examples in Financial Services are – Multi-Channel Customer Journey Analytics, Customer Network Analytics, Marketing Attribution, Path to Churn, Predict Complaints, Customer Satisfaction/Net Promoter Score (NPS), Identify Broken/sub optimal Processes, Path to Fraud, Fraud Networks, Claims Fraud, Online Fraud, Collection Analytics, Pre-default risk, risk based pricing, Risk Model Lift, Sales Compliance & Mis-Selling, Call Centre Analytics, Behavioral Analytics….and lots more!
A couple of examples are shared below, where we are working with organisations in the finance & retail sector:
Financial Services: Our customer Aditya Birla Financial Services Group leverages Teradata analytics solution that enables them to create a single source for data and insights for its entire business, across multiple group companies. The solution helps ABFSG to gain a better understanding of customers across the financial services businesses and help provide better, more relevant and more timely service to their end customers. This since repository of data bring together transactional, financial and risk management data. In terms of results, maximizing customer value through democratization of data across different departments and functions and then operationalizing it has been a major differentiator at ABFSG. This has helped deliver cost savings, including improved consolidated reporting, reduced external audit fees, improved product pricing and improved campaign ROI. The company also sees benefits in terms of storage savings and processing improvements, along with the extra competences and services they can now offer customers. Additionally, IVR and customer-care call-centre operations are two areas where ABFSG has used web services to leverage Teradata. The solution has helped their team members to make more accurate calculations and decisions.
Retail- Retailing is set to be data-driven as never before. Big Data and analytics bring to the table critical customer and business insights through actionable insights, drive performance and fuel growth across all forms of retail platforms – stores, mobile shopping sites and e-commerce channels. This is important because in today’s connected world, customers are looking to have a seamless experience at their disposal, from physical stores to the online, digital shopping experience or even a mix and match of the two.
Reaching out to customers effectively means a presence across both these worlds, and this is optimally done by using insights gleaned through analytics that combs through Big Data. Collection, storage, organizing and analyzing customer data is critical and makes for personalized customer interaction and there is no better justification for this than the knowledge that 8 out of the 10 top global retailers and 18 out of the 20 top U.S. retailers use such technology! In India we are working with a large retailer in a program where they get to “Know their customer better”
The first is about Customer knowledge, wherein Big Data helps
1) know your customer,
2) their preferences with regard to brands and brand variants,
3) mode of shopping,
4) pricing choices,
5) when they shop –
all details that tell you about your customer’s shopping behaviour so as to enable you target them better. This helps them in recommending personalized offers to their customers.
Operations is the next front on which enterprises are helped through Big Data analytics. A knowledge of customer shopping behaviour and helping them find their choices better and more conveniently is all a very visible part of the exercise, but getting this is done means a huge back-end operations job that involves logistics, inventory, merchandising, time and space – none of which are visible but count for much. Having a product they like and where they want to buy it from means setting the supply chain in place from before a product gets to be made to making it available at a time and place where the knowledge of shopping behaviour allows retailing to make this happen best.
5. Where and how should players in the Indian payments & merchants ecosystem start when looking at using Data & Analytics more effectively?
It goes back to the earlier discussion above, broadly they should look at analytics around Customer Experience and Operational Excellence. Both these areas are very large and multiple use cases exist today around these. The best recommended way is to first understand their existing infrastructure. Teradata has multiple consulting services where we help customers identify what is the best course for adoption of their data journey, we identify gaps in their existing infrastructure and recommend the best Analytics tools and technologies to adopt to embark. Also, it is very important to note some of these points below:
1) When organisations have just started their big data and analytics journey, they must consider their computational and technical capabilities. Excessive churning of large amounts of data can become exhausting for most powerful of systems if there are other workloads being performed simultaneously on the same system.
2) Companies should increasingly innovate and revolutionize as big data has the ability to change everything and anything. Efficient big data toolsets enable low cost, scalable, high-performance and near real-time analytics. Also, important to note is to prioritise what business use cases they are looking at, you cannot do everything at once. Start with your most critical requirements and expand them as you go.
3) Organisations across verticals and sectors operate on decisions generated basis insights from data analytics and with more data being produced and churned they must not lose their way in between. It is important for these companies to shell out the relevant and significant data and related insights and connections to be able to answer all the business questions and take better decisions.
4) Build a proper analytics culture within the business to embrace the value of analytics so that they are able to trust that data and the resulting insights, instead of just creating a big data lake comprising of data gathered from all possible sources but which is not helping in getting any outcomes.
5) Ensure the data models being implemented are operational and the algorithms involved can drive insightful decision making and business actions.
6. Give us a sneak peek into what you’ll be presenting at Spot Forum 2017 (taking place on 29 Nov 2017 at Sofitel BKC Mumbai?
Our focus is going to be around leveraging Artificial Intelligence and Deep Learning in the area of Customer Analytics and Fraud/Risk by understanding patterns in data and applying advanced techniques to uncover insights, we’ll also go through real-life customers who have gone down this path with Teradata and value they’ve derived. Focus of the session will be on Business Priorities in this area and how you can achieve those outcomes.
7. Why are you excited to be participating in the conference?
We’re excited to participate since it allows us to connect with organizations that are dealing with issues on a daily basis that can be potentially solved by leveraging analytics, It also gives us an opportunity to share best practices and our experience in the data and analytics space.