Adopting big data analytics is emerging as a key business necessity to remain relevant and profitable for firms in the data-intensive financial sector. To get a deeper insight into benefits and relevance of big data analytics in financial markets and know more about how Indian banks are riding the big data wave, Dataquest interacted with Saurabh Banerjee, Senior Specialist, Platforms, Sapient Global Markets. Edited Excerpt:
On a daily basis, the financial markets generate large amounts of unstructured data. In this context, what is the relevance of big data analytics in this segment?
Big data analytics helps in generating actionable insights to improve strategic and operational decisions. Data-driven insights are indispensable for developing innovative business models, products, and solutions. It also helps in optimizing operating costs by better targeting of products and service delivery.
For example, customer intent prediction using historical call records, past interactions on web and social media, click-stream analysis, etc, can enable support agents in effectively responding to customer queries. Similarly, insights from micro-segmentation and customer profiling enables financial institutions to deliver personalized customer experience, improve customer retention, and maximize cross-sell and up-sell opportunities.
Leading insurers are exploring ways to leverage telematics data to offer personalized insurance policies that reward good driving behavior. Lenders are experimenting with dynamic pricing of loans that maximizes profit while controlling risk of default using predictive analytics. Trade surveillance systems are integrating real-time data from news and social media to build a 360 degree view of market activity, sentiments, and trading behavior.
According to you, what is the maturity level of big data analytics in the Indian financial ecosystem? Can you cite some examples of the financial firms benefiting from big data adoption?
The Indian financial ecosystem is still in the early stages of adoption of big data analytics with banks taking a lead.
Retail lenders such as ICICI and HDFC Bank process thousands of loan applications every hour. One of the biggest concerns for any retail bank is administering a sound credit underwriting policy. Banks have started utilizing big data analytics to analyze a wide spectrum of customer data to decide credit worthiness and go beyond credit score from the bureaus. Recently, analytics enabled HDFC Bank to deploy straight-through processing of personal loans without human intervention and with regards to this, HDFC Bank’s personal loans business has been growing at 25-30%.
Using analytics for debt recovery and collection programs can help reduce collection costs, increase debt recovery levels, and retain good clients. ICICI Bank improved collection efficiency by developing a centralized debtors’ allocation model to allocate delinquent cases to the right channels till the last mile.
Big data analytics is a key enabler for digital transformation for Indian banks. For instance, Kotak Mahindra Bank utilized advanced analytics to classify dormant accounts into different groups and focus reviving efforts on customers who are more likely to reactivate their accounts.
Analytics is used by all major banks for Anti-Money Laundering (AML) and fraud detection by identifying suspicious activity such as moving money to multiple accounts, large single-day cash deposits, opening a number of accounts in a short period of time, or sudden activity in long-dormant accounts. For example, ING Vysya Bank uses data modelling and neural network scoring engine to generate accurate reports, detect frauds and to adhere to AML guidelines.
Banks are also using analytics to determine where ATM branches should be positioned and how much cash should be placed in them. Citibank received an award for ATM cash forecasting during Nasscom Big Data Analytics Summit, 2015 in Hyderabad.
Insurance companies will be forced to invest in big data analytics as they face pressure to perform and become profitable. Utilizing big data analytics to understand individual behavior enables optimal product pricing, fraud detection, claims settlements, and new product development.
Earlier this year, Birla Sun Life Insurance signed a 9-year outsourcing deal with IBM to consolidate, redesign in-scope applications, and use analytics to provide client insights that build competitive advantage. However, analytics adoption in insurance remains abysmally low compared to global peers.
In the wake of recent scams like National Spot Exchange payment crisis, how can big data analytics help the regulators like Securities and Exchange Commission, Forward Markets Commission, and Reserve Bank of India to curb the menace?
Big reforms in India often follow big crisis. To address these threats, regulators like RBI are moving towards more data-centric risk-based supervision. The move towards risk-based supervision with more granular and credible data collection processes will help in the optimal
use of scarce supervisory resources.
SEBI records transaction and master data from various exchanges for trade surveillance. Towards second half of 2011, SEBI started using analytical models (using SAS Enterprise Miner) to identify known market manipulation patterns, eg, circular trading, pump and dump, insider trading, and front running. SEBI already has a sophisticated surveillance system, which generates at least 100 alerts of suspicious trading activities every day. The surveillance systems also track media reports for information being shared among the investors that appear suspicious and in violation to the SEBI regulations and model codes of conduct for various entities, including listed companies and market intermediaries.
SEBI is putting in place enhanced software tools to help in its fraud surveillance activities by monitoring suspicious trades and also analyzing information in public domain like social media and news. It is also considering a proposal to conduct profiling of major clients or investors in various segments to understand the pattern of their market participation and their impact.SEBI has adopted a risk-based supervision model and developed a methodology for assessing risk levels for various market intermediaries, including stock brokers, depository participants, mutual funds, custodians,merchant bankers, portfolio managers, registrars and transfer agents, credit rating agencies, and investment advisors.
Going ahead, what trends are expected to emerge in big data and analytics in the financial markets? How can they tap into the true potential of big data?
Financial market players would need to adopt big data analytics to remain relevant and profitable in a hyper competitive business environment. Organizations will need to align their people, processes, and technology platforms to provide highly personalized customer experience by extracting insights from data in real time from a wide variety of data sources.
Data has become the new oil to drive decision making. Organizations will have to collaborate to leverage ecosystem data from key partners in the value chain.Board members and C-suite executives would need to be educated to ask the right questions and empower senior executives to make strategic and operational decisions based on data-driven insights to maximize profits and drive market share for improved shareholder value. During the last financial crisis, many financial institutions and regulators were mostly unaware of their risk exposures.
Now regulators can leverage sophisticated big data analytics to automate market surveillance, conduct simulations and stress testing for timely detection of systemic threats, and to protect consumers from market manipulations, money laundering, and fraud.