Analytix Industry White Paper - Big Data Accelerates Earnings Growth in Banking and Insurance

Big Data Accelerates Earnings Growth in Banking and Insurance

Few industries depend as heavily on data as financial services for making capital allocation decisions. Banks and insurance companies aggregate, price and distribute capital with the aim of increasing their return on capital, in the short and long term, within an acceptable risk level. To competitively do that, they need real-time, actionable data that will accurately read and forecast the market, as well as screen for fraud and regulatory risks. In todays marketplace, this means gathering, analyzing and storing millions of transactions a minute. Recognizing this, financial service companies outpace all other sectors in their purchases of data solutions.

In spite of these levels of investment, corporate data is slow to access, of questionable quality, inconsistent, siloed and expensive to manage and maintain. Data solutions are inadequate given the increasing burden of governance, risk and compliance exemplified by more rigorous stress testing and capital adequacy. Solutions are also not swift enough to spot increasingly sophisticated fraud patterns found in credit cards and payment networks. Lastly, executives need answers to their inquiries without waiting weeks and spending millions of dollars.

Forward-thinking financial services firms have responded to these challenges by embracing Hadoop to gain an edge in the quest for risk-adjusted return on capital. A survey of C-level Wall Street executives conducted by NewVantage Partners found that for the first time, Wall Street is seeing that Big Data is having an even greater impact on how they do business than initially imagined. Early adopters within the financial services world are realizing impressive benefits. The time to generate a critical business answer is moving from months or weeks to hours and minutes as a result of the incorporation of big data into their processes. The survey cites the following specific benefits realized:

Operational data stores built for $300,000 in Hadoop versus $4,000,000 using relational databases

Trading warehouse built for $200,000 in Hadoop versus $4,000,000 with a database appliance

Analyzing risk data in 3 hours versus 3 months Pricing calculations performed in 20 minutes versus 48 hours Behavioral analytics in 20 minutes versus 72 hours Modeling automation from 150 models per year to 15,000 models per year.

Analytix.is Industry White Paper Big Data in Financial Services

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Hadoop makes all this possible. Specifically, Hadoop delivers

New Opportunities for Analytics: The Schema On Read architecture of Hadoop empowers users to store any data in its raw format. Only once analysts choose to reason over the data does it become necessary to define a schema to suit the needs of their application.

Multi-use, Multi-workload Data Processing: Hadoop supports multiple access methods (batch, real-time, streaming, in-memory, etc.) to a common data set. Analysts can view and transform data in multiple ways. Closed-loop analytics bring time-to-insight closer to real-time than ever before.

New Efficiencies for Data Architecture: Hadoop runs on low-cost commodity servers with direct attached storage that allows for a dramatically lower overall cost of storage. This makes it affordable to retain all source data for very long periods, thus providing applications with far greater historical context.

Data Warehouse Optimization: Hadoop provides a much lower cost environment than data warehouses for the ETL function. Data is extracted and transformed in Hadoop. Only the results are loaded into the data warehouse, which can use more of its CPU cycles and storage space to perform truly high value analytics.

That explains why many banks and insurance companies throughout the world are adopting Hadoop. Hadoop has proven itself in every major functional area in this industry: Function Use case Retail Banking Screen New Account Applications for Risk of

Default Underwriting and Claims Processing

Improve Underwriting Efficiency for Usage-Based Auto Insurance

Analyze Insurance Claims with a Shared Data Lake Capital Markets, Trading and Asset Management

Maintaining SLAs for Equity Trading Information Trade Surveillance and Compliance Analysis Mining Data Assets with an Enterprise-Wide Data

Lake New and Adjacent Businesses

Monetize Consumer Finance Data for Investors, Advertisers and Merchants

This white paper illustrates real-life improvements in business performance achieved by Hadoop in these 7 discrete use cases. Banks and insurers extract the most value from Hadoop when they focus on solving specific line-of-business challenges, such as those outlined here. This complements the value that Hadoop creates in horizontal functions such as IT, which is not covered in this white paper.


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Retail Banking

Screen New Account Applications for Risk of Default

Business Challenge

Every day, large retail banks receive thousands of applications for new checking and savings accounts. Bankers that accept these applications consult 3rd party risk scoring services before opening an account. They can (and do) override do-not-open recommendations for applicants with poor banking histories.

Many of these high-risk accounts overdraw and charge-off due to mismanagement or fraud, costing banks millions of dollars in losses. Some of this cost is passed on to other customers who responsibly manage their accounts.

Solution

Apache Hadoop can store and analyze multiple data streams and help bank managers control new account risk in their branches. They can match banker decisions with the risk information presented at the time of decision. This allows them to manage risk better by sanctioning individuals, updating policies, and identifying patterns of fraud.

Over time, the accumulated data informs algorithms that may detect subtle, high-risk behavior patterns unseen by the banks risk analysts.

Value Realized

Improved risk management allows the bank to lower its provisions for bad debts and write-offs.

Underwriting and Claims Processing

Improve Underwriting Efficiency for Usage-Based Auto Insurance

Business Challenge

Traditional auto insurance attempts to differentiate and reward safe drivers for their historical driving recordsthe accidents and traffic infractions that have (or have not) already happened.


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That raises the question whether a particular driver with a good driving record has merely been lucky, or is in fact a prudent driver.

A property and casualty insurance company with $17B in revenue and 28,000 employees illustrates the challenge.

Newer usage-based insurance (also called Pay as You Drive, or PAYD) attempts to align premiums with empirical risk, based on how policyholders actually drive. Safer drivers pay less, because the insurance company actually knows how they drive. Because policyholders know this, PAYD insurance promotes a virtuous cycle that improves overall safety and reduces moral hazard amongst drivers who take more risk on the road because they know that theyre covered.

Advances in GPS and telemetry technologies have reduced the cost of capturing the driving data used to price PAYD policies, but the data streaming from vehicles grows very quickly, and it needs to be stored for analysis.

The growing volume, velocity and variety of incoming data taxed their existing systems and processes. The insurer was storing its PAYD data on an RDBMS platform, but storage costs were too high, so the company only retained 25% of the available data. Processing that subset of data took one working week. Risk analysis was too slow.

Solution

After adopting Hadoop, the company retains 100% of policyholders PAYD geo-location data and processes that quadrupled data stream in three days or less. More data and faster processing enables the insurer to price risks better. It can retain low-risk drivers that might have churned because other insurers were offering better rates. And it can re-price high-risk drivers so that they become sustainably profitable for the insurer.

Value Realized

The insurer is able to acquire certain segments the prudent drivers with more affordable rates, and is able to more profitably serve that segment since risky drivers are re-priced based on their behavior.


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Analyze Insurance Claims with a Shared Data Lake

Business Challenge

A major provider of property, casualty, life and mortgage insurance with $65B in revenue, 60,000 employees and operations in 100 countries illustrates the potential of Hadoop for claims processing.

The company already had systems in place for analyzing structured data at scale. Less-structured claims notes or social media analysis was used on a claim-by-claim basis, but it did not scale easily. Combining all textual or social data with all structured data was not economically viable, yet had the potential to add valuable information to claims analysis.

Solution

Apache Hadoop changed that. It is a schema on read architecture that permits ingest of a much wider range of data types. Data puddles that were previously scattered about are now unified in a data lake, for a much clearer and holistic picture needed to process a claim. This deep data reservoir can still be analyzed using existing business intelligence tools and employee skills, thanks to close integration between Hadoop and existing BI assets such as SAS, Tableau and QlikView.

Value Realized

Hadoop allows the insurer to blend and correlate data from various sources using a variety of processing engines and analytical applications. This is of crucial importance when combatting claims fraud because what may appear as a legitimate claim in one system is quickly exposed as a fraud when additional structured and unstructured data from different sources are brought to bear.

Capital Markets, Trading and Asset Management

Maintaining SLAs for Equity Trading Information

Business Challenge

A leading New York based provider of market data with 15,000 employees offers the real-time information backbone relied on for trading equities and other financial instruments by


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hundreds of thousands of market participants. This ticker plant collects and processes massive data streams, displaying prices for traders and feeding computerized trading systems fast enough to capture opportunities in seconds. This is useful for making real-time decisions. Years of historical market data are also available for long-term analysis of market trends. The provider was ingesting 50GB of server log data from 10,000 feeds daily. Four times daily, this data is pushed into DB2. Applications query the data 35,000 times per second. 70% of queries are for data less than 1 year old, 30% for data more than one year old. The specific challenge was two-fold: The existing architecture was only able to hold 10 years of trading data. And the growing volume of data was degrading the performance, with a risk of missing an SLA of 12 milliseconds.

Solution

The provider re-architected their ticker plant with Hadoop as its cornerstone. ETL offloading to Hadoop provided affordable long-term data retention. And serving queries from Apache HBase provided ultra-low latency that meets the rigorous SLA requirements.

Value Realized

The market data provider realized a more than ten times improvement in price-performance for this particular area of their business.

Trade Surveillance and Compliance Analysis

Business Challenge

An investment services firm with $16B in assets and 4,000 financial advisors serving millions of individual clients processes fifteen million transactions and three hundred thousand trades every day. The specific challenge was two-fold: The existing architecture was only able to hold a limited period of data online, which means that analyses of historical data were only possible with a cumbersome restore of the data from archive. More importantly, each days trading data was not available for risk analysis until after the close of business. This created an unacceptable window of time where the firm was exposed to risks from rogue trading without a timely way to intervene while improper trading was happening. Intraday risk analysis was very limited.


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Solution

Hadoop accelerates a firms speed-to-analytics and also extends its data retention timeline. A shared data repository across multiple lines of business provides more visibility into all intra-day trading activities. The trading risk group accesses this shared data lake to processes more position, execution and balance data. They can do this analysis on data from the current workday, and it is available for at least five yearsmuch longer than before. Moreover, Hadoop enables ingest of data from recent acquisitions despite disparate data definitions and infrastructures.

Value Realized

The data lake accelerates time-to-insight and extends retention. Operational data is available to risk analysts while markets are still open, enabling them to reduce risk of that days trading activities.

Mining Data Assets with an Enterprise-Wide Data Lake

Business Challenge

A leading global investment services company with $1.5 trillion in assets under management, $14 billion in revenue and 50,000 employees wanted to capitalize on its disparate data assets which were largely unavailable across the organization. Current enterprise data warehouse solutions were appropriate for some data workloads but too expensive for others, such as sever logs. Financial log data is difficult to aggregate and analyze at scale. The high cost of legacy technology means typical retention periods are short, which hampers analysis of prices and performance over longer periods. This is relevant for analyses such as lifetime customer value and cost to serve.

Solution

The company deployed a multi-tenant Hadoop cluster to merge data across groups. Server log data for instance was merged with structured data to uncover trends across assets, traders and customers. Sensitive data was accessed via Accumulo part of the Hortonworks distribution of Apache Hadoop - which enforces read permissions on individual data cells.

Value Realized

The company started mining its data assets with this enterprise-wide data lake and expects literally more than a hundred use cases to emerge over time. Already, the project has more than covered its cost since Hadoop right-sizes enterprise data warehouses. Moreover, Hadoop


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delivers better insights already on customer acquisition costs or longer-term patterns in various corners of the financial market.

New and Adjacent Businesses

Monetize Consumer Finance Data for Investors, Advertisers and Merchants

Business Challenge

One of the largest US financial institutions was looking for a way to monetize aggregate consumer finance data. Banks possess massive amounts of operational, transactional and balance data that holds information about macro-economic trends. This information can be valuable for investors, advertisers and merchants.

The specific technical challenge in generating revenue from this data was two-fold: Data protection regulations and policies require that the privacy of bank customers is strictly protected. This requires valuable banking data to be aggregated and served in a way that does not contain personally identifiable information. Moreover, the data that was of interest lived in isolated legacy silos controlled by different lines of business.

Solution

The financial institution turned to Hadoop as a common cross-company data lake for data from different lines of business, covering mortgages, consumer checking, personal credit, wholesale transactions and treasury banking. A single point of security and privacy enforcement allows the bank to operationalize security and privacy measures such as de-identification, masking, encryption, user authentication and access control.

Value Realized

Both internal bank executives and consumers in the secondary market derive value from the data. Mortgage bankers, consumer bankers, credit card group and treasury bankers have access to the same cross-sell data. As a result, the bank is able to improve operational decision making but also generate revenue from the sale of actionable intelligence to investors, advertisers and merchants.

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Any financial services business cares about minimizing risk and maximizing opportunity. Banks weigh the risk of opening accounts and extending credit against the opportunity to hold deposits. Insurance companies balance the risk of claims outpacing premiums. Investment companies pursue long-term portfolio appreciation knowing that some securities will lose value.

Storing and processing all data in Hadoop provides better insight into the optimal balance of risk and opportunity. With Hadoop, financial services firms can build a competitive advantage by improving their risk management, reducing fraud, driving customer upsell and improving investment decisions.

These financial services company examples show what enterprises across industries are discovering: Hadoop brings both superior economics compared to legacy analytics, data warehousing and storage alternatives as well as exciting new capabilities. These capabilities provide deeper and more actionable insights to drive revenue up and costs down.

Documents

Analytix Industry White Paper - Big Data Accelerates Earnings Growth in Banking and Insurance