Commentaries and insightful analyses on the world of finance, technology and IT.

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April 30, 2014

The influence of business intelligence on consumer and commercial lending

The financial industry has become increasingly dependent on IT tools and techniques. From marketing, prospect identification and customer acquisition to product lifecycle management and customer management, virtually every single process today is driven by information technology. Of late, banks have begun combining IT tools and techniques with analytics to improve efficiencies and drive better outcomes. The end goal is entirely customer-focused - improve customer trust, loyalty and delight. 

In the area of credit, it is now the norm for banks to possess enormous quantities of data that need to be stored. The size of this accumulated data continues to grow exponentially and, if leveraged properly, can help provide meaningful business direction and enable better decision making. However, due to the sensitive nature of credit data, it is mandatory to comply with regulatory requirements to avoid potential data ion protect breaches or an adverse impact on data security. Therefore, financial institutions have started segregating data based on confidentiality and accessibility. As a result, the questions that now need to answered are based on confidentiality levels of the data and power of access. Once the data is secure, it is then time to think about utilizing it's power and leveraging the underlying information within the data. Often, historical data helps provide valuable insights. Historical data talks about frequency, consistency, repeat behavior, trend, most or least, highest or lowest, etc. When subjected to extract, transform, load (ETL), data begins to take on a tangible "shape" and can provide very accurate direction, beside helping answer several questions like:

  • What's the current trend?
  • Where should the business focus be?
  • What parts of the business fare well and what parts do not?
  • When is the next change needed?
  • How can we satisfy the customer's requirements?

 

Identifying business needs

The most crucial aspect is to leverage BI to benefit your business as well as your customer. The first major step in this direction is to convert raw data into actionable information. With the help of BI frameworks, you can identify quality data, extract the right information from it and create reports that contain usable information for the business. This information can help decision makers make the right decision at the right time.

Re-engineering and consolidating business processes


The credit process includes several business modules like marketing & sales, loan origination, underwriting, fulfillment and closing & monitoring. Each module consists of several business process entities, actors and actions. For ease of management and maintenance, each credit module is considered an individual application. This has made each application move in different directions over a period of time. Some applications are coded in Java while others use DotNet. Some applications continue to use the legacy system of access database while others use advanced SQL Server. With the passage of time, as the process becomes more mature and customers become more knowledgeable, these different directions cause a conflict in interest. Higher maintenance, low compatibility and inadequate integrity demands need for reengineering and consolidation of business process. Business intelligence provides enterprises with the right path for business process reengineering, in addition to making sure that the initiative addresses all concerns identified through historical data and business intelligence.


Lessons from hands-on experience

We have partnered with clients and re-engineered their business processes for several legacy applications to resolve conflicts. These conflicts arose due to isolated individual applications, inconsistent and bulky data volumes, and lack of systematic processes to convert data to information. We introduced business intelligence tools into the system and, to their surprise the clients began reaping the benefits within a short span of time. There were changes and business process reengineering activities initiated by the meaningful direction provided by these tools. Through business reengineering and process consolidation, the customer started experiencing more benefits than expected. The steps followed to implement the solution are:

  • Analyzed several applications used within client organization in the Commercial and Consumer lending domain
  • Implemented business intelligence tools across applications in scope to gather meaningful data
  • Identified applications and processes to be streamlined based on the direction received from the BI tools
  • Qualified applications went through business process reengineering and business process consolidation
  • Improved data integrity and reusability through data consolidation and single view

The success of this systematic solution was the result of the close partnership with the client and the ability to leverage subject matter expertize from both sides. Actions were driven by data and results were driven by action. The client was able to address existing business issues and significantly enhance user experience. Infosys helped reengineering the user interface based on several attributes that were obtained through a thorough analysis of product-related data. These solutions resulted in the streamlining of legacy applications and created a single window view of the data and applications across the entire commercial and consumer lending domain.

In today's information-driven world, data is one of the most valuable resources an organization can possess. However, the data is only of value if your institution can analyze it to derive actionable insights and drive tangible, measurable improvements - for the customer as well as yourself. Leveraging the power of business intelligence to reengineer business processes can help you reduce operational costs and financial risk.

April 21, 2014

Big data use cases in financial services

In a hyper-competitive, customer-driven environment, Financial Services Institutions (FSIs) must capitalize on internal and external data sources to gain an accurate understanding of customers, markets, products, services, channels, and competitors. In addition to structured data, a vast amount of unstructured but valuable data is generated through social media. FSIs must index, consume, and integrate structured and unstructured data using big data technology to realize the value of data.

The big data market is worth over US$ 5 billion and is expected to exceed US$ 50 billion by 2017. Over 2.5 quintillion bytes of data is generated daily. With rapid advances in technologies like MapReduce, Hadoop, NoSQL, and the cloud, there is significant innovation in data. In addition, the cost of hardware (e.g., NAS-based storage, in-memory data grids/ RAM, etc.) is reducing. Further, software-enabled storage products are now available at reasonable prices. A combination of these factors facilitates highly scalable architecture required for big data implementations. 

 

Let me highlight key use cases of big data technology for FSIs:

1. Risk management: Big data helps FSIs manage liquidity, credit, default, enterprise, counterparty, reputational, and other risks. It also enables centralized risk data management. Real-time individual risk profiles can be created for customers based on their social networking activities, purchase behavior, and transaction data.
 
Big data can help meet regulatory requirements in a cost-effective manner. Regulatory mandates require storing and analyzing transactional data dating back several years. Big data helps build dynamic data structures that comply with changing reporting requirements. It also enables instant analysis of risk scenarios for institutions with growing data volumes.

A comprehensive view of aggregated counterparty risk exposures, positions, and impact enhances performance and reduces default. Big data helps analyze behavior profiles, cultural/ demographic segments, and spending habits of customers to enhance the lender's risk management capability. Predictive credit risk models based on a large amount of payment data helps prioritize collection activities. In addition, market events across regions can be captured and insights gleaned in real time from news, audios, visuals, and social media.

2. Fraud detection: Big data can help in fraud mitigation, Know Your Customer (KYC) and Anti-money Laundering (AML) monitoring, and rouge trading/ insider trading prevention programs. Big data analysis enables detection of deviation from a standard pattern of customer behavior for proactive fraud identification and prevention. For instance, real-time outlier detection and analysis can be undertaken for a credit card used in distant locations within a short span of time. Similarly, real-time analysis of transactions based on diverse data sets is possible. When fraud is anticipated, the transaction can be blocked even before it takes place. Significantly, big data can help in ATM fraud reduction through proactive analysis of geographical and other data points, and identification of ATMs that are likely to be targeted by fraudsters.

3. Customer delight: Big data can help FSIs better understand the needs of their customers. Petabytes of data can be analyzed in real time to deliver bespoke services and products to customers. Real-time analysis of unstructured data from social media and other sources enables customer and trading sentiment analysis (find out how customers feel about a new product/ service, or assess influencers and customer sentiment in response to broad economic trends/ specific market indicators). FSIs will be able to better manage their brand image by proactively anticipating customer needs and issues, and responding to negative opinions.

Big data aids in micro-level understanding of clients and enables targeted and personalized offers. Significantly, it offers a 360-degree view of the customer. Issue resolution at contact centers can be improved through real-time analysis of unstructured data (voice recordings) for content quality, sentiment analysis, and trends and patterns identification. Internal customer logs and social media updates can be analyzed to identify customer sentiment and dissatisfaction points for timely action. Big data can recommend robust call center data integration with transaction data to reduce customer churn, enhance up-sell and cross-sell; and enable proactive alerts. It facilitates extraction of unstructured information from IVR and other customer service systems, and enables blending of internal data with social media inputs.

4. Sales enhancement and cost reduction: FSIs can gain useful insights into when and where customers use their credit/ debit cards, and customer behavior patterns from big data. Based on the monitoring of customer behavior, FSIs can take predictive actions and enhance their cross-sell and up-sell capabilities. Sentiment analysis-enabled lead management and sales forecasting can be initiated through social media analytics. It can also facilitate real-time and proactive micro-segmentation, and smart location-based offerings.

Several FSIs are challenged by legacy systems that are costly to maintain. These institutions can migrate their legacy data to integrated big data platforms and add valuable data sources to mine rich and valuable insights. Operational efficiencies can be further improved with big data platforms that enable monitoring and analysis of transactional and unstructured data (voice recognition, social media comments, and e-mails). The workload at financial service enterprises can be predicted and staffing needs in branches and call centers can be optimized.
 
5. Operations and execution: The operations of FSIs that have undergone mergers and acquisitions can be challenging. New core infrastructure solutions enabled by big data can streamline operations. For example, big data enables standardization of loan servicing time across channels and entities. In addition, institutions can adopt data processing approaches and optimize the supply chain. Enterprise payments hub optimization provides a better view of payments platform utilization.

 

Big data can improve operational capabilities of FSIs and enhance global, regional and local services. Real-time insights from transactions help provide the right services to customers and at the right price using the right channel. Capital markets firms have multiple data sources and data silos across the front, middle and back office. Big data allows operational data store consolidation. When data tagging is undertaken using big data, trades/ events can be identified, thereby preventing duplicate, invalid or missed trades. Big data enables storage of a large quantity of historical market data and allows feeding dynamic trading predictive models and forecasts. It also facilitates analysis of complex securities with market, reference and transaction data from diverse sources. In addition, organizational intelligence can be improved through employee collaboration analytics.

 

Have you taken the big leap yet?

April 1, 2014

The US Mortgage Industry Outlook

 
Regulatory impact

The mortgage industry in the US has undergone a significant transformation post the global financial crisis. As part of the Dodd-Frank Wall Street Reform and Consumer Protection Act, the Consumer Financial Protection Bureau (CFPB) was formed in 2011. The CFPB has been very active in rule-making since then. One of the landmark events was the coming into effect of the changes to the Truth in Lending Act on 10th January 2014. The changes included the Ability-to-Repay and Qualified Mortgage Standards rules, which are expected to bring uniformity in the products offered by various lenders to borrowers.

Government-sponsored enterprise (GSE) reform

A majority of the mortgage loans today are guaranteed by GSEs Fannie Mae and Freddie Mac. The various policy announcements clearly indicate that there is clear consensus among authorities about reforming GSEs, reducing the role of government in guaranteeing mortgages and bringing private players back in the market. Work is already underway on building a new Common Securitization Platform (CSP) that will replace the two disparate platforms from Fannie and Freddie. This is another major event that is being tracked closely by the mortgage industry since it will require them to make significant changes to mortgage securitization and investor accounting practices.

Shift in originations from re-finance to purchase

The originations sector of mortgage lending faces headwinds after enjoying years of strong growth led by re-financing due to low interest rates. With rates expected to rise, lenders need to shift their focus to purchase loans, which require different sales techniques as compared to re-finance. In this challenging scenario, mortgage companies will need to show innovation to achieve growth. The role of IT will be a differentiator as newer channels, such as the Internet and mobile, will be critical to increase volumes and reduce cycle time to close a loan. As we see an increase in the millennial generation as first time buyers, lenders will need to look at these channels, which will have higher adoption rates with this demographic. The Qualified Mortgage rule from CFPB, which took effect in January 2014, and the soon to be announced Know Before You Owe rule from CFPB, scheduled to come into effect on August 2015 makes it even more critical for mortgage lenders to build the right processes and systems for originating and closing mortgage loans in compliance.

Summary

Choosing the right technology and business partner for mortgage lenders is now more critical than ever given the significant challenges expected in the future. We are already witnessing churn in the mortgage industry, leading to some companies gaining an edge over the rest of the pack by being more agile in responding to the changes in the market and leveraging innovative technology and business solutions to differentiate themselves.