BANKING, FINANCIAL SERVICES, AND INSURANCE (BFSI)
Banks and other financial institutions gather large volumes of data every day. The BFSI vertical is undergoing large-scale transformations as banks, credit card companies, investment funds, insurance companies, and government-funded enterprises are facing continuous challenges in offering value added services for customers and launching new products. The BFSI vertical has to handle challenges such as rising costs because of numerous regulatory bodies including the US Office of the Comptroller of the Currency (OCC), Federal Reserve Board (FRB), Consumer Financial Protection Bureau (CFPB), European Central Bank, and Reserve Bank of Australia. They also need to cascade complex control requirements and handle costs effectively, across the supply and services networks. Organizations in the banking, financial services, and insurance vertical, are running the risk of default in payment or repayment, and volatility across the trading market. Risk Analytics, financial analytics, and customer analytics are some of the major predictive analytics solution that are utilized by companies operating in the BFSI vertical.
Additionally, banking and financial services organizations have to face frauds at different levels which vary from purchases made by stolen credit card, money laundering, first-party fraud, and insurance claims. Predictive analytics also complements an organization’s current transaction monitoring systems, by tracking frauds before the occurrence.
COMPETITIVE LEADERSHIP MAPPING TERMINOLOGY
The vendors of predictive analytics software in BFSI are placed into 4 categories based on their performance in each criterion: “visionary leaders,” “innovators,” “dynamic differentiators,” and “emerging companies.” The top 23 vendors evaluated in the data quality tools market include Agilone, Alteryx, Inc, Angoss Software Corporation, Dataiku, Domino Data Lab, Exago, Inc., Fair Isaac Corporation (Fico), Good Data, Greenwave Systems, Inc, IBM Corporation, Information Builders, Inc., Knime Ag, Kognitio, Microsoft Corporation, NTT Data Corporation, Oracle Corporation, Qliktech, Inc., Rapidminer, Inc, Sap Se, Sas Institute Inc, Sisense, Tableau Software Inc, Teradata Corporation and Tibco Software Inc
Uses Cases of Predictive Analytics Software in BFSI
- Customer experience management / Customer churn: Predictive Analytics is used to analyze repository of past customer queries and feedback. The insights are used to prevent customer issues in future by improving customer related processes.
- Fraud analytics: Based on past customer records (such as transactional data, credit history, etc.) probable fraudulent cases are predicted. Banking institutions can use such insights for offering loans, ensuring loan paybacks and prevent financial crimes.
- Personalized marketing / increasing sales: Using customer specific insights (such as buying patterns, purchase history, preferences, spending capabilities, etc.), BFSI organizations can target their messaging around their offerings for increasing sales.
Case Studies of Predictive Analytics Software in BFSI
Case Study: Improving customer satisfaction issues in real time for Bank of Ireland
Bank of Ireland’s Retail Strategy Marketing was focusing on improving customer incentive program by analyzing huge data associated with consumer sentiment records. It aimed to increase operational efficiency of data collection processes as well. Bank of Ireland leveraged capabilities of IBM SPSS for improved data collation and analysis along with associated automation processes.
- Production cycle time decreased by approximately 66% and analysts could be utilized for high value activities
- Bank developed capabilities to parse the verbatim of customer collected in each cycle. It can utilize the collected customer feedback in more efficient manner for quantitative analysis.
- TAT for Ad-hoc requests of internal customers is reduced to hours rather than days.
Case Study: RSA Canada implemented SAS solutions to increase the accuracy of pricing models
The information technology department of RSA Canada combines the data coming from different systems into a single view. Company wanted to implement a solution to charge different rates for different risk characteristics, and standardize the effect on the total premium in the pricing model. The company feds the data into the company’s SAS server so the actuarial department can build the premium rating models.
- SAS solution is used in all aspects of rating model construction
- The solution was deployed for building predictive models, manipulating these models for business implementation and monitoring the implementation results.
- Improved decile analysis
- SAS gives RSA Canada the ability to easily add new risk factors
Case Study: Religare Health Insurance enhanced business control, resolved bank statements at a faster rate, and ensures GST compliance with Oracle ERP Cloud
Religare Health Insurance (RHI), a specialized player in providing health insurance products, including travel and personal accident. The company aims to deliver solutions that benefit customers, distributors, employees, and shareholders. RHI needed an integrated platform to improve process efficiency and comply with the tax regulations. RHI configured Oracle ERP Cloud based on the needs of its health insurance business. RHI also created project documentation and conducted pilot testing and user interface prototyping.
- Modernized ERP platform with new technology and practices help RHI gain visibility to efficiently manage the entire financial and supply chain processes
- RHI meet compliance requirements
- Faster and accurate payment transmission
- Reduced manual intervention and automated the entire risk-free business process
Case Study: PNC Financial Services utilizing personalized real time marketing for improved customer experience
PNC Financial Services aimed to offer rich customer experience, raise its revenue with personalized offers and eliminate the time consuming manual processes hampering their decision-making. Hence, a comprehensive solution was required to coordinate all customer real-time interactions across all channels. The bank utilized Pega’s business process management and real-time decision making capabilities for creating a centralized hub for managing all customer treatments across all channels.
- PNC Financial Services witnessed immediate revenue uplift.
- It was ranked as the no 1 for customer experience amongst northeast banks; while nationally it ranked 2nd in banking customer experience
- It also helped to ensure consistent automated processes across the enterprise
Palantir Technologies, Inc.
Case Study: A global bank making smart, data-driven decisions for preventing foreclosure of troubled home assets
A global bank managing a multibillion dollar home lending portfolio of delinquent mortgages required to prevent foreclosures, modifications, and cancellations of troubled home assets. Hence, an efficient solution was required to manage several systems of incomplete records and analyze huge data sources to understand the home lending portfolios for further avoiding losses during the mortgage crisis. The bank employed Palantir’s enterprise-wide analytics and visualization tools to build advanced automated model, and market forecasts for making smart-data driven decisions.
- Hundreds of millions of dollars saved by reducing borrower’s debt, henceforth preventing unavoidable foreclosures
- Up-to date and accurate loan level pricing
- Increased efficiency of bank’s short sales processes
- The bank fulfilled national economic recovery objectives, motivated local communities, and successfully tackled drivers of financial crisis
Case Study: Improving book roll process and carrier relationship for Reliable Insurance Agency
Reliable Insurance Agency providing insurance and financial expertise to the inhabitants of Duluth/ Superior area of Minnesota needed to automate the time consuming, inefficient manual book rolling for improving carrier relationships and communication. The agency leveraged the advantages of Vertafore’s Book Roll Analytics to automate the entire book roll process and successfully managed to save significant amount of time.
- Eliminated the necessity for key-staff to conduct the book roll through fully automated book roll process
- Improved carrier relationship and communication
Case Study: Länsförsäkringar Bank utilizing machine data to enhance customer experience
Länsförsäkringar Bank, a Swedish Bank aimed at collaborating its machine data into a unified platform needed an exquisite solution to gain deeper insight into its operations. The bank utilized Splunk’s Enterprise platform, and Splunk DB Connect to collect and analyze data generated via its online services and mobile applications for improving customer support, analyzing customer behavior and developing new services.
- Bank achieved faster resolution of service issues
- Successfully enhanced customer experience
- Gained deeper insights into customer’s demands, and subsequently developing new services
Case Study: Clear sales forecast across a growing selection of products and expanding sales force
A Financial Data Services Company maintains information on more than 220 million companies worldwide and licenses this information for use in credit decisions, business-to-business marketing and supply chain management.
With a sales org of over 1,000 reps, the Company did not have a clear outlook on its sales forecast. Combined with hundreds of thousands of customers, an ever-growing selection of products, and an expanding sales force, the executive team had difficulty aligning and agreeing on priorities. Objectives were to:
- Understand sales metrics across a complex sales organization
- Incorporate scenario and predictive analysis for better fidelity
- Run a data-driven sales organization
- Hundreds of reports on sales, product performance, orders, deals, products, etc.
- Reports for 1000+ sales people
- Replace BOBJ and Cloud9 reporting solutions
- Birst automated predictive capabilities: pipeline snapshots, matching with conforming dimensions (orders), and slowly changing dimensions (territory changes)
- Robust integration with Salesforce
- Automatic aggregation of multiple data sources to provide a full view of sales – from historical to future
- Successfully delivering sales analytics to 1000+ sales reps
- Improved sales performance by targeting deals & products with the highest chance to close
Case Study: HDFC ERGO adopted SAP Ariba Sourcing to optimize savings and efficient supplier negotiations
HDFC Ergo wanted to keep growing by having greater process efficiency particularly in sourcing. The company turned its attention to automation and single integrated sourcing platform to deliver a fairer and clearer process for all suppliers. The main objectives were to improve sourcing visibility, validation, and cost savings, migrate to a centralized sourcing program to add value to the company by securing the best cost through online sourcing and enhance the transparency of agreements made with suppliers and negotiated terms.
- Greater credibility and fairness for suppliers through consistent and clear online negotiations
- Improved cost optimization and supplier management
- More-efficient documentation and record keeping
- A strategic savings of around 15% to 20%
Case Study: Westpac New Zealand upgraded its on-premise CRM environment using Microsoft Dynamics 365 to build a customer-centric banking strategy
Westpac New Zealand wanted to build more valuable relationships with its banking customers by upgrading its aging on-premises CRM environment. The company aims to forge new ways to do business, make more meaningful connections with customers, and compete effectively. Westpac find it hard to differentiate on products and services, so Westpac set out to compete by building more valuable customer relationships. The bank struggled with manual processes, duplicated effort, and missed opportunities for more productive customer engagements. The company turned its attention to cloud based Microsoft Dynamics 365 to follow a more consistent approach to customer onboarding, follow-up, and personalization without having to enter data multiple times into multiple systems.
- Dynamics 365, hosted in Microsoft Azure went live for the commercial banking division and will be available throughout the bank by 2019
- Real time customer data help in creating more meaningful customer connections
- Reduce operational cost and effort, and generate new revenue opportunities
- Westpac can easily track the customers moved across channels within the bank, from online or retail banking or credit services to wealth management, investment, or commercial banking
Case Study: Improving customer experience
Client, a large lending Non-Banking Financial Company (NBFC) wanted to build a framework to scale the experience given to high net worth individuals to the entire customer universe. The client collaborated with BRIDGEi2i to build a hierarchy of machine learning models to predict next best product in next 12 months and next best product for 3-5 years. Build segmentation to predict the lines for customers basis inference from bureau. Solution has been implemented real-time on the customer facing client’s application and for the sales force to aid good customer service BRIDGEi2i proprietary personalization platform was used to execute the project. The platform was hosted in Azure (client environment) and was integrated with data base and the front-end User Interface (UI) layer.
- Improved the marketing efficiency by 25% Year Over Year (YoY)
- Helped in decreasing the “Not interested” action by customer thereby decreasing “Do Not Contact” proportion and indirectly boosting Net Promoter Score.
Case Study: To optimize investigation process for identifying additional premium opportunities TBA
A worker compensation insurance company wanted to optimize its investigation process for identifying additional premium collection opportunities. The current investigation process in the company for identifying additional premium opportunities was not efficient and did not meet its Return on Investment (RoI). The insurance provider collaborated with PrADS to address the problem. PrADS developed an action plan after the assessment and review of the current investigation process in the company, evaluation of data points used, identification of improvement areas to gain efficiency, validation of existing models, building a new analytics model using customer’s and Dun & Bradstreet (D&B) business data, and defining useful data variables for future procurement. PrADS helped the insurance provider by developing a predictive model to estimate the probability of a customer paying additional premium after combining customer data with D&B data to estimate the expected yield and recommend the optimum cut off point.
The PrADS solution enabled the insurance company in identifying the customers with high probability of paying additional premium and optimizing its audit process with the following benefits resulting in significant RoI increase:
- Cost of investigation reduced by USD 3.5 million
- USD 12 million collected as additional premium
Case Study: Improving customer experience with real-time customer insights
The Commonwealth Bank of Australia, an Australian multinational bank, provides a variety of financial services including retail, business and institutional banking, insurance, funds management, investment and broking services. The bank serves the customers spread across Australia, New Zealand, US and UK. The bank is aiming to provide enhanced experience to its retail customers with better recommendations about its products and services. The banks has developed a mobile app to take advantage of big data and deliver superior results to customers. This app has made possible to analyze the transactional data and find insights about merchants, customer profitability, cash flow, and key markets among others. The bank has added analytics capabilities from MicroStrategy to gain accurate and real-time insights about its customers.
- The mobile app developed by Commonwealth Bank enabled the small and medium-sized enterprise customers to not only monitor their performance in real-time and identify purchasing and demographic trends.
- The mobile app also helped the bank to build loyalty and trust with across its clients, allowing bank to improve customer satisfaction.
Case Study: Improvised management reporting and decision support systems with predictive analytics capabilities
Promutuel Insurance, leading property and casualty insurance provider in Canada, offers variety of insurance products. The company offers personalized quality service to more than 6,30,000 insureds and employs 1,910 people. The company deployed predictive analytics solutions from Guidewire Software to create analytics-based agency prospecting tool to appoint agents in high potential areas to reach untapped markets.
- The addition of predictive analytics capabilities to get real-time insights into its management reporting and decision support systems.
Case Study: Monitoring corporate financial risk with Qlik Technologies solutions
Leading Italian bank Mediocredito’s specialized in consulting, advanced finance and leasing and merchant banking catering its services to small and medium sized banks. The bank was facing challenges to provide targeted corporate analysis and consultation as well as monitor the financial risk. The bank was also needling solution to view and interpret data in simple and easy-to-understand manner. The bank connected with Qlik Technologies to implement predictive analytics solutions to provide common dashboard to its employees and manage financial risk across platforms from rate risk, treasury risk, ALM risk and operating risk.
- The bank improvised its abilities to analyze and monitor financial risks across ALM, rate risks, treasury risk, and operating risk via one system X-Match.
- The bank improvised processing speed and output.
Case Study: Leading Banking and Insurance company using BI solution to improve the operational efficiency
The leading BFSI organization based in Europe needed to deal with various challenges, including increasing ownership cost, report proliferation with self-service BI, stringent data policies, and high pressure on ETL batch window due to increasing data volume. Hence, a comprehensive solution was required to resolve these issues. The bank utilized Hexaware’s Solution Accelerato, BIMA capabilities for reducing the overall cost.
- Reduced cost of licensing
- Improved productivity of IT team
- Improved performance and storage of data warehouse
Case Study: Digitization of business process of DHFL Pramerica Life Insurance
DHFL Pramerica Life Insurance is the India based insurance company needed to digitize the administrative activities, provide quick access to the varied insurance policies and maintain the high customer service across the industry. The insurance company turned to OpenText Corporation and deployed the OpenText AppWorks, OpenText Content Suite, OpenText Managed Services, and OpenText Professional Services. These products helped the insurance company digitize end-to-end business process.
- Digitized business process
- Lower processing cost by 20% to 30%
- Minimum transaction time
Case Study: To meet the EDI compliance challenges
Liberty Mutual needed to comply with Electronic Data Interchange (EDI) by keeping update on advanced compliances and rules of EDI and make changes accordingly. Moreover, the insurance company did not have any internal tool to analyze and measure the compliance internally. Thus, Liberty Mutual used wcAnalyzer Compliance Cube to track the compliances issues and minimize the cost and time consumed during the process.
- Tracking the compliance issues
- Linking the analytics to injury report side in reduced time
- Real time access to the EDI data.
Case Study: To meet the EDI compliance challenges
A large lending Non-Banking Financial Company (NBFC) wanted to build framework to scale the experience given to high net worth individuals to the entire customer universe. The client collaborated with BRIDGEi2i to build the hierarchy of machine learning models to predict next best product in next 12 months, next best product for 3-5 years, and building segmentation to predict the lines for customers basis interference from bureau. Solution has been implemented real-time on the customer facing client’s application and for the sales-force to aid good customer service.
- Improved the marketing efficiency by 25% YoY
- Helped in decreasing the Not Interested action by customers
Frequently Asked Questions
How will the Predictive Analytics Market perform in near future?The predictive analytics market size is expected to grow from USD 4.6 billion in 2017 to USD 12.4 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 22.17% during the forecast period. Proliferation of internet and the availability of various means for accessing the internet have led to a massive increase in the data volumes being generated. This will help in the advancement and expansion of high-speed internet services.
What are the opportunities in the predictive analytics market?With the rise in touchpoint and the need for collecting data to understand consumer behavior, every touch by a consumer has become an important data point that can be processed to reveal user behavior. With the exponential rise in individual and organizational data, businesses are now deploying teams of data scientists and analysts to process the collected data. Another factor accelerating adoption is the revenue generating potential of predictive analytics. This is compelling firms to invest in predictive analytics.
What is the competitive landscape in the market?The predictive analytics ecosystem comprises vendors, such as Alteryx, Inc. (US), AgilOne (US), Angoss Software Corporation (Canada), Domino Data Lab (US), Dataiku (US), Exago, Inc. (US), Fair Isaac Corporation (FICO) (US), GoodData Corporation (US), International Business Machines (IBM) Corporation (US), Information Builders (US), Kognitio Ltd. (UK), KNIME.com AG (Switzerland), MicroStrategy, Inc. (US), Microsoft Corporation (US), NTT DATA Corporation (Japan), Oracle Corporation (US), Predixion Software (US), RapidMiner (US), QlikTech International (US), Sisense, Inc. (US), SAP SE (Germany), SAS Institute, Inc. (US), Tableau Software, Inc. (US), TIBCO Software, Inc. (US), and Teradata Corporation (US). The exponential growth in data volume is due to the expansion of businesses worldwide, which is driving the rise in data volumes and sources. The accumulation of big data in a single location has rapidly developed the evaluation capabilities of data science experts in every organization. Additionally, companies prefer to provide stand-alone solutions rather than combined solutions. This is eventually resulting in a rise in the number of big data analytics startups, which are driving noteworthy innovations.
What are the regulations that will impact the market?Predictive analytics leads to ad hoc analysis, which assists companies to have all workable solutions for their business specific questions and forecast past, present, and possible future predictive scenarios. In the current competitive business scenarios, companies need more than accurate predictive statements and reports from its predictive analytics. Companies now need more forward-looking, predictive insights that can help them shape impactful business strategy and improve the day-to-day decision-making in real time.
How are mergers and acquisitions evolving the market?July 2017, SAP collaborated with energy and services company Centrica to help their customers in managing assets and energy consumption on insights available through the IoT. February 2017, Oracle announced the expansion of its IoT portfolio with the introduction of 4 new cloud solutions to assist businesses to fully utilize the advantages of the digital supply chain. By applying advanced predictive analytics to devise signals, IoT applications can help in automating business processes and operations across the supply chain to enhance customer experience. March 2016, the company has extended their strategic partnership to offer combined capabilities of cloud analytics and big data to their users. This will help users to automate and simplify the decisions while attaining greater business insights for smarter business decisions.
What are the dynamics of the market?Proliferation of internet and the availability of various means for accessing the internet have led to a massive increase in the data volumes being generated. This will help in the advancement and expansion of high-speed internet services. Globalization and economic growth are also playing major roles in driving greater data generation worldwide. Also, the rise in connected and integrated technologies has provided a platform to predictive analytics software vendors for leveraging this development and the unprecedented growth of the internet. Additionally, the eCommerce sector has modified the traditional shopping behavior of customers. Dedicated email campaigns, online/social media advertising, and cognitive analyzing of customers are the key enablers driving sales and increasing customers’ loyalty. With connected devices coming to the forefront, retailers are focusing on real-time analysis of customers’ shopping behavior and market basket analysis for analyzing consumers’ perception, which can be used for building tailor-made offers to increase customer retention. Similarly, with the rise in the global IoT analytics demand in the retail sector, the market is expected to have unprecedented growth opportunities for predictive analytics.