big data in banking and financial services pdf

Big Data In Banking And Financial Services Pdf

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Digitization in the finance industry has enabled technology such as advanced analytics, machine learning , AI , big data , and the cloud to penetrate and transform how financial institutions are competing in the market. Large companies are embracing these technologies to execute digital transformation, meet consumer demand, and bolster profit and loss.

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Current landscape and influence of big data on finance

It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle s products remains at the sole discretion of Oracle.

Recently, bank profitability has been on the rise, especially in regions of the world where economic conditions are good.

Financial services organizations will continue to focus on revenue growth and higher margins through operational efficiency, better risk management, and improved customer intimacy. Banks will also develop new revenue streams by entering new markets and service areas.

Using information technology to enhance the customers experience in both retail and business banking can help grow interest-based and fee-based revenue. Many larger financial organizations are gravitating towards expansion of wealth management portfolios to ensure lower risk and consistent fee-based revenue.

Differentiated services, cross-sell and up-sell initiatives, and expansion into emerging wealth-management markets around the world are on the rise. Analytics and information management play a central role in ensuring that these strategies are properly executed. As financial services companies embark on a journey to gain a better understanding of customers and their household preferences in order to provide effective and differentiated services, the amount of data grows, data collection occurs more frequently, and data variety becomes more complex.

Information discovery tools enable them to rapidly combine various data sets leading to better insight. They often want more data to be ingested at higher rates and stored longer, and want to analyze the growing data volumes faster. Big Data solutions help financial services and banking institutions respond to these requirements. This paper provides an overview for the adoption of Big Data and analytic capabilities as part of a next-generation architecture that can meet the needs of the dynamic financial services and banking industries.

This white paper also presents a reference architecture introduction. The approach and guidance offered is the byproduct of hundreds of customer projects and highlights the decisions that customers face in the course of architecture planning and implementation. The paper reflects the experience of Oracle s enterprise architects who work within many industries and who have developed a standardized methodology based on enterprise architecture best practices.

The addition of Big Data systems enables organizations to gain much higher levels of insight into data faster and enables more effective decision making. An Enterprise Modeling Platform Financial services organizations are rethinking how they model their enterprises. This renewed focus is fueled in part by new regulatory requirements.

In addition, financial institutions are increasingly incorporating analytical insights into their operational decision processes.

Statistical modeling is taking on a wider role within the enterprise as institutions weave prediction, optimization, and forecasting models into their enterprise analytics fabric.

New challenges come as adoption increases. As output from the models becomes part of regulatory and other business intelligence processes, enterprise model management much like enterprise data management must become a priority.

When modeling becomes more prevalent, the models are often deployed on centrally managed platforms in IT that are aligned to individual lines of business. However, there can be a chasm between the modeling and IT worlds.

Modeling platforms often contain copies of enterprise data. While a bank may have put in place sophisticated data governance policies around data in the enterprise warehouse, data used for models often falls outside the purview of these governance systems. The problem is exacerbated by the new sources of data that modelers want access to. The oft-repeated phrase that the analytics problem is a data problem underscores the need to closely link analytics and data management. Yet while banks have poured resources into enterprise level data management and governance programs, enterprise-level model management does not seem to have attracted the same level of attention.

Just as regulatory requirements shaped the financial institutions data management approach, we believe that regulators demands for model management will be very similar. Risk and Capital Management Traditional enterprise architectures have served banks and financial services companies well for years. The architectures have enabled these institutions to manage credit, market liquidity, and operational risk.

In addition, these systems have enabled the institutions to manage their capital and meet Basel regulatory requirements. Credit and behavioral scoring to classify new or existing customers for credit worthiness required significant analysis of loan application data and data from credit bureaus by credit experts.

Given the push of banks into micro-credit and the expansion into the emerging markets, the scarcity of available credit data is hugely problematic. This data scarcity can be overcome through predictive modeling using non-traditional input from peer groups, P2P payment data from mobile devices, utility consumption and payment data, prepaid mobile services purchase data, and other sources.

The valuation of complex and illiquid instruments and portfolios requires simulation of thousands of risk factors using stochastic models. Monte Carlo simulations are frequently used.

As the number of risk factors increase, the compute power required for these simulations increases exponentially to the number of risk factors. Big Data technologies enable the simulations to run to conclusion at a low cost by introducing commodity hardware and largely open source software.

Banking analysts are building data models that can predict loans likely to become delinquent in order to initiate proactive action. Large banks in the United States are initiating transfer of servicing rights to sub servicing organizations for specific loans in default. Identifying loans that are to be moved to sub servicing organizations requires detailed analysis of various data elements such as history of the loan, the borrower, and the loan documents.

Banks sometimes use predictive analytics to create heat maps that identify regions that are known for mortgage fraud, showing details at a zip code level and possibly at an individual level. This enables effective analysis when the appraisal process is being conducted for a new loan application and can help prevent potential property valuation, occupancy, and short-sale fraud.

Big Data technologies can help in the identification of household spending patterns by building a richer view of customers.

Banks can analyze transaction logs for all their products and identify spending trends at the household level. This provides a better view of the customer s true ability to pay back loans and also helps identify future crossselling and up-selling opportunities. Portfolio managers usually receive news alerts about companies in their holdings.

In order to respond, the portfolio manager then needs to shift through massive amounts of data to determine if a shift in allocations is warranted. They would like to 1 detect changes in financial conversations about companies in the portfolio, 2 investigate the conversations to determine source of information, 3 compare social information with internal information, and 4 identify all funds containing the companies and also identify the allocations.

These types of analytical capabilities can be gained quickly through Big Data and related solutions. Improving Customer Intimacy Banks and financial services companies seek to differentiate themselves by developing and delivering unique products and services for their customers.

However in this very competitive industry, successful products are often copied and the customer s barrier to exit is very low.

Ten years ago, a person was more likely to have a long term relationship with a bank enabling the bank to dictate the terms for current accounts, savings accounts, and mortgages. That person might have had another relationship with a discount brokerage, where the brokerage would have controlled the fee structure, the margin requirements and CD rates.

The suppliers were at the center of these relationships. Today, this individual likely has multiple transient relationships with a number of banks, including an account at a bank that charges no fees, accounts in banks that offer the highest interest on savings, and mortgage loans from banks offering the lowest mortgage interest rate.

The customer is now the center of attention, with the financial institutions being transient nodes. The growth in the power of the customer is recognized by new entrants in the market institutions built around a complete digital footprint. These institutions have raised the expectations of the individual consumer. The consumer now expects to have full transparency about the products and services being offered. For banks and financial services companies to keep customers for the long term, they must get closer to them.

They must anticipate customer needs and be able to proactively position their products. When they fail, customers will choose to get financial services elsewhere. Over time, the customers might entirely leave the institution. The Internet of Things IoT introduces new consumer options connecting customers with other service providers such as retailers, airlines, and hotels.

Financial services companies are developing partnerships with many of these to extend their reach and integrate their products into all areas of their customers lives. Creating a seamless, consistent experience across multiple channels can lead to a superior customer experience and drive enhanced revenue opportunities. Consider the case of Jane Doe. Jane is a customer of the XYZ bank.

Jane is single and direct deposits her pay into a checking account. If the bank was monitoring her social media activity, they might know that she just got engaged. Positioning a loan for a wedding might be something that Jane would be interested in. Monitoring Jane s social media activity enables the bank to anticipate Jane s needs.

The relationship could later grow to offering a mortgage for Jane s first house, plans for her children, college for her children, and continue all the way to Jane s retirement. When social media data for an individual is not available, data about the peer group that they belong to might be used instead e.

Improving Fraud Detection Fraud detection and prevention is facilitated by analyzing transactional data and interdicting an incoming real-time stream of transactions against a well known set of patterns.

Big Data technologies enable correlation of data from multiple sources or incidents to determine fraud. The individual incidents by themselves may not signal a fraudulent event.

An example of suspicious activity might occur when a trader consistently sends an or calls a telephone number within a few minutes of making a large trade. Adding new data points such as geo-location data can enhance fraud prevention an ATM card being used in New York to withdraw cash while the mobile device of the customer is active in London is an indication of a likely fraudulent event. Modern self-adaptive machine learning algorithms can learn and track behaviors of customers and devices enabling identification of fraud early.

IT organizations at banking and financial services companies typically work with their lines of business to build solutions that deliver the following when defining Big Data projects: 1 Enterprise Modeling and Analytics Platforms: Banks are building data reservoirs as places to store data extracts from all operational and non-traditional data sources. Business users and analysts explore the data in the data reservoir and develop analytic business models in a self-service environment.

Big Data technologies have been applied successfully in a number of financial services use cases, but the enterprise level use of Big Data for firmwide analytic problems remains a challenge. Building an enterprise analytics platform gives users controlled access to all the data to explore it, build models, and deploy the models.

Banks have an incredible amount of information available about the buying behavior of their customers. When combined with the location of the customer, it is possible to drive the customer to visit a merchant location.

For example, a time bound offer for a local restaurant that has a relationship with the bank can be made to a customer via their mobile device as they walk into a movie theatre. The value of the customer continues to grow as more and more services are sold to them.

Social media can be a good source of data to get a head start on life events including, graduation, first job, engagement, weddings, college costs and retirement. This insight can enable more products to be sold by getting the right product in front of the consumer at the right time.

Better understanding of the customer, their traits, how they like to communicate, services they consume, and their value to the business enables the right product to be positioned to the customer at the right time for the right price. IT operational efficiency is often difficult to prove but is sometimes an initial justification that IT organizations gravitate toward when deploying these types of solutions.

On the next page, we show a table that summarizes several typical business challenges in financial services and banking companies and illustrates the opportunity for new or enhanced business capability when adding new analytic capabilities.

Big data, ethics and financial services: risks, controls and opportunities

Welcome to Finextra. We use cookies to help us to deliver our services. We'll assume you're ok with this, but you may change your preferences at our Cookie Centre. Please read our Privacy Policy. However, since the ultimate business goal of Big Data is not the data itself, but to get business insights into the data, the analytics part of the chain is the most visible and important for a business user, which explains why the terms are often interchanged. According to a study of IBM in , it is estimated that every day we create 2. Banks have enormous amounts of customer data i.

It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle s products remains at the sole discretion of Oracle. Recently, bank profitability has been on the rise, especially in regions of the world where economic conditions are good. Financial services organizations will continue to focus on revenue growth and higher margins through operational efficiency, better risk management, and improved customer intimacy. Banks will also develop new revenue streams by entering new markets and service areas.

The finance and insurance sector by nature has been an intensively data-driven industry, managing large quantities of customer data and with areas such as capital market trading having used data analytics for some time. The advent of big data in financial services can bring numerous advantages to financial institutions: enhanced levels of customer insight, engagement, and experience through the digitization of financial products and services and with the increasing trend of customers interacting with brands or organizations in the digital space; enhanced fraud detection and prevention capabilities through the use of big data it is now possible to use larger datasets to identify trends that indicate fraud; and enhanced market trading analysis, where trading strategies which make the use of sophisticated computer algorithms to rapidly trade the financial markets. This chapter identifies the drivers related with the evolution of the sector, like the impact of regulations, and changing business models, together with the associated constraints related with legacy culture and infrastructures, and data privacy and security issues. The findings, after analysing the requirements and the technologies currently available, show that there are still research challenges to develop the technologies to their full potential in order to provide competitive and effective solutions. The finance and insurance sector by nature has been an intensively data-driven industry for many years, with financial institutes having managed large quantities of customer data and using data analytics in areas such as capital market trading. The business of insurance is based on the analysis of data to understand and effectively evaluate risk.

In particular, banking industry has evolved from just journal and ledger entry paradigm to data and analytics driven banking operations, which subsumes online.

Big Data in Financial Services and Banking

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Metrics details. Big data is one of the most recent business and technical issues in the age of technology. Hundreds of millions of events occur every day.

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Сеньор?. - Буисан, - сказал Беккер.  - Мигель Буисан. - Понятно.

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Big data, ethics and financial services: risks, controls and opportunities

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Big Data in Finance – Your Guide to Financial Data Analysis



Ac-cording to a report by Researchmoz (), apart from the government sector, the financial services sector has more to gain from leveraging big data; the.


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