The Opportunity for Predictive Analytics in Finance

By Sue Korn

April 21, 2011

It is often said that managing enterprise risk and micro risk is about finding the needle in the haystack. Predictive analytics uses powerful computers with large memory and storage to eliminate 90 percent of the hay, those “easy” decisions that a computer can handle effortlessly. The modeling systems then score the remaining 10 percent, prioritizing the activities of the human analysts and investigators to do what they do best, which is to make the optimal decision.

That entails such things as finding the best risk/reward trade-offs for new customers, avoiding fraudulent insurance claims, identifying fraud or abuse in government programs, stopping questionable transactions, and optimally pricing assets against the degree of risk.

Predictive analytics is the discipline that uses computational techniques to search for ways to optimize business decisions. Applications in financial services include front-end customer acquisition analytics, offer selection, relationship management, pricing optimization, risk management, fraud management, and actuarial analysis for insurance.

High Performance Business Computing in Financial Markets

Financial services is the second-largest commercial high performance computing (HPC) vertical market, second only to manufacturing. It is also one of the fastest growing, and as a result, it is a critical part of our High Performance Business Computing (HPBC) methodology. Within financial services, high-frequency trading is the most well-known application, but there are several other areas where HPC is in use.

Intersect360 Research tracks a number of broad application areas as part of the financial services vertical. These include trading, both high-frequency trading and algorithmic trading; risk management, at the enterprise, portfolio or customer level, as well as actuarial analysis for insurance; pricing and valuation of individual securities, derivatives, and compound derivatives; and business and economic analytics, including modeling, simulation, and decision support.

Financial services companies take many forms, from large, multinational, multiline organizations to regional boutiques. An individual company might run all or none of these application types. (You cannot guarantee that a given bank runs HPC risk management applications any more than you can guarantee that any manufacturer runs HPC computer-aided engineering simulations.) But among these application types, analytics, particularly predictive analytics, is important for its potential to be leveraged in multiple ways.

There are several different levels of predictive analytics techniques used, with increasing levels of sophistication. At the simplest level, traditional techniques such as regression, linear modeling, rules-based algorithms and decision trees are used. More complex techniques such as neural networks and machine learning are at the next level. Newer techniques include text analysis (where, for example, notes entered by a service representative after a customer calls in can be mined or sentiment can be coaxed out of tweets) and social network analysis (looking for patterns in the relationship between a customer and provider, in context of all other customers and providers).

These individual techniques can be combined into compound engines such as net lift (or uplift) modeling, where two or more scenarios are analyzed simultaneously to trace all possible outcomes and choose the right treatment (or lack of treatment) for a particular situation. There’s also ensemble modeling, in which a suite of models are run and the final response comes from a weighting of the individual models’ results, and where the model-weighting can also be refined based on the situation.

We expect that analytics will be a growing market for what we call High Performance Business Computing (HPBC), particularly in financial services and related disciplines. There are three legs to the stool supporting this belief. First, there is an explosion of data becoming available, both internal and external, to organizations. Second, there are methodologies to analyze and make sense of this vast amount of data are being developed and improved every day. The third leg of the stool is the availability of cost-effective and accessible systems (in terms of computational speed, data storage, memory) to be able to do something useful with it. Put these three legs together and you get a large potential opportunity for HPBC.

The systems required to perform predictive analytics range from Excel using a SAS dataset on a laptop computer, all the way to custom-designed, self-tuning engines running on large clusters or in-database, and everything in between. On one extreme, predictive analytics is clearly using high performance computing. On the other extreme, it clearly is not. Where to draw that line right now is less important than the conclusion that more and more companies are moving towards these sophisticated techniques.

Industry leaders have their own internal teams, and this capability provides a differentiating competitive advantage. Those who have not made the switch will be evaluating these techniques and systems with more interest as more and more success stories are written by those using predictive analytics.

Companies moving to predictive analytics will get there in one of two ways, either building teams internally or by hiring third-party providers to develop their systems for them. These third parties can use the principal company’s systems, or can run the analytics on behalf of the principals, sending back scores and metrics to be loaded onto the principal company’s internal database.

Why Predictive Analytics

Financial institutions do not sell widgets, take in revenue on those sales and pay a cost of making the good that they sold. While manufacturing companies can build a better product (better quality at a better price) using digital manufacturing, financial institutions’ assets are monetary in nature. In contrast to a manufacturing organization, financial institutions make their money on the spread, or difference, between what they earn on their financial assets and what they pay for their liabilities. This spread also has to be enough to cover their operating expenses, which generally include credit losses, fraud losses and fraud management.

Assets, in this sense, are insurance policies that provide premium income. They can be loans that provide origination fees, finance charges and service fees. They can be investment portfolios that provide management fees or trading revenue. Liabilities could be deposits or debt where the institution is paying a rate of interest for the use of the depositor or investor’s money.

A financial institution maximizes this profit calculation through two mechanisms: risk management and pricing optimization. Risk management encompasses the institution’s initial decision to originate a loan or insurance policy, their ongoing behavior analysis (e.g., fraud, delinquency, late payment, increased claims) and exposure management, like not renewing a policy or implementing line reductions. On the other side is pricing optimization, which includes the initial pricing decision, whether to do special offers or provide discounts to entice profitable customers to stay or deepen their relationship, and the implement ion of the penalty pricing (e.g., if the customer goes over their limit or pays late).

The analytically elite companies have these types of analytics as part of their DNA. They are constantly loading new transaction or behavior data, evaluating assumptions, calibrating models, rebalancing among methodologies, reweighting results in ensemble infrastructures. “Constantly” used to mean monthly not too long ago. Increasingly it means weekly, daily or even as transactions are initiated.

Predictive Analytics Beyond Banking

Although financial services institutions are among the most advanced users, the potential benefits are available to many business areas. Already, predictive analytics are also making a difference in non-financial markets. For example in the government arena, it’s being used to reduce waste, identify fraud in government programs, and uncover tax fraud. In health care, it’s being employed for cost management, system fraud, and more accurate or quicker diagnoses. Finally in telecom, predictive analytics is being used to minimize customer base churn.

On that last point, basically any company has groups of customers it would like to manage, both in terms of customer relationship management (CRM) issues like customer acquisition and turnover, as well as tailoring product portfolios and pricing to different categories of customers.

Because of its broad potential applicability, predictive analytics should continue to be a significant growth driver for HPBC markets. The vast amount of data being collected by companies virtually guarantees that there are some valuable nuggets of information waiting to be brought to light that can have a material impact on profitability. Finding these needles in the haystack is a challenge, but predictive analytics provides a way for companies to take advantage of them.

About the Author

Sue Korn is a senior analyst at Intersect360 Research specializing in High Performance Business Computing (HPBC) applications, and a 20-year veteran of the financial services industry. In her role at Intersect360 Research, Korn spearheads the company’s analysis of the drivers and barriers of HPC adoption in business environments and the growing role of HPBC applications.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

Activist Investor Starboard Buys 10.7% Stake in Mellanox; Sale Possible?

November 20, 2017

Starboard Value has reportedly taken a 10.7 percent stake in interconnect specialist Mellanox Technologies, and according to the Wall Street Journal, has urged the company “to improve its margins and stock and explore Read more…

By John Russell

Installation of Sierra Supercomputer Steams Along at LLNL

November 20, 2017

Sierra, the 125 petaflops (peak) machine based on IBM’s Power9 chip being built at Lawrence Livermore National Laboratory, sometimes takes a back seat to Summit, the ~200 petaflops system being built at Oak Ridge Natio Read more…

By John Russell

SC Bids Farewell to Denver, Heads to Dallas for 30th

November 17, 2017

After a jam-packed four-day expo and intensive six-day technical program, SC17 has wrapped up another successful event that brought together nearly 13,000 visitors to the Colorado Convention Center in Denver for the larg Read more…

By Tiffany Trader

HPE Extreme Performance Solutions

Harness Scalable Petabyte Storage with HPE Apollo 4510 and HPE StoreEver

As a growing number of connected devices challenges IT departments to rapidly collect, manage, and store troves of data, organizations must adopt a new generation of IT to help them operate quickly and intelligently. Read more…

SC17 Keynote – HPC Powers SKA Efforts to Peer Deep into the Cosmos

November 17, 2017

This week’s SC17 keynote – Life, the Universe and Computing: The Story of the SKA Telescope – was a powerful pitch for the potential of Big Science projects that also showcased the foundational role of high performance computing in modern science. It was also visually stunning. Read more…

By John Russell

SC Bids Farewell to Denver, Heads to Dallas for 30th

November 17, 2017

After a jam-packed four-day expo and intensive six-day technical program, SC17 has wrapped up another successful event that brought together nearly 13,000 visit Read more…

By Tiffany Trader

SC17 Keynote – HPC Powers SKA Efforts to Peer Deep into the Cosmos

November 17, 2017

This week’s SC17 keynote – Life, the Universe and Computing: The Story of the SKA Telescope – was a powerful pitch for the potential of Big Science projects that also showcased the foundational role of high performance computing in modern science. It was also visually stunning. Read more…

By John Russell

How Cities Use HPC at the Edge to Get Smarter

November 17, 2017

Cities are sensoring up, collecting vast troves of data that they’re running through predictive models and using the insights to solve problems that, in some Read more…

By Doug Black

Student Cluster LINPACK Record Shattered! More LINs Packed Than Ever before!

November 16, 2017

Nanyang Technological University, the pride of Singapore, utterly destroyed the Student Cluster Competition LINPACK record by posting a score of 51.77 TFlop/s a Read more…

By Dan Olds

Hyperion Market Update: ‘Decent’ Growth Led by HPE; AI Transparency a Risk Issue

November 15, 2017

The HPC market update from Hyperion Research (formerly IDC) at the annual SC conference is a business and social “must,” and this year’s presentation at S Read more…

By Doug Black

Nvidia Focuses Its Cloud Containers on HPC Applications

November 14, 2017

Having migrated its top-of-the-line datacenter GPU to the largest cloud vendors, Nvidia is touting its Volta architecture for a range of scientific computing ta Read more…

By George Leopold

HPE Launches ARM-based Apollo System for HPC, AI

November 14, 2017

HPE doubled down on its memory-driven computing vision while expanding its processor portfolio with the announcement yesterday of the company’s first ARM-base Read more…

By Doug Black

OpenACC Shines in Global Climate/Weather Codes

November 14, 2017

OpenACC, the directive-based parallel programming model used mostly for porting codes to GPUs for use on heterogeneous systems, came to SC17 touting impressive Read more…

By John Russell

US Coalesces Plans for First Exascale Supercomputer: Aurora in 2021

September 27, 2017

At the Advanced Scientific Computing Advisory Committee (ASCAC) meeting, in Arlington, Va., yesterday (Sept. 26), it was revealed that the "Aurora" supercompute Read more…

By Tiffany Trader

NERSC Scales Scientific Deep Learning to 15 Petaflops

August 28, 2017

A collaborative effort between Intel, NERSC and Stanford has delivered the first 15-petaflops deep learning software running on HPC platforms and is, according Read more…

By Rob Farber

Oracle Layoffs Reportedly Hit SPARC and Solaris Hard

September 7, 2017

Oracle’s latest layoffs have many wondering if this is the end of the line for the SPARC processor and Solaris OS development. As reported by multiple sources Read more…

By John Russell

AMD Showcases Growing Portfolio of EPYC and Radeon-based Systems at SC17

November 13, 2017

AMD’s charge back into HPC and the datacenter is on full display at SC17. Having launched the EPYC processor line in June along with its MI25 GPU the focus he Read more…

By John Russell

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

Google Releases Deeplearn.js to Further Democratize Machine Learning

August 17, 2017

Spreading the use of machine learning tools is one of the goals of Google’s PAIR (People + AI Research) initiative, which was introduced in early July. Last w Read more…

By John Russell

GlobalFoundries Puts Wind in AMD’s Sails with 12nm FinFET

September 24, 2017

From its annual tech conference last week (Sept. 20), where GlobalFoundries welcomed more than 600 semiconductor professionals (reaching the Santa Clara venue Read more…

By Tiffany Trader

Amazon Debuts New AMD-based GPU Instances for Graphics Acceleration

September 12, 2017

Last week Amazon Web Services (AWS) streaming service, AppStream 2.0, introduced a new GPU instance called Graphics Design intended to accelerate graphics. The Read more…

By John Russell

Leading Solution Providers

EU Funds 20 Million Euro ARM+FPGA Exascale Project

September 7, 2017

At the Barcelona Supercomputer Centre on Wednesday (Sept. 6), 16 partners gathered to launch the EuroEXA project, which invests €20 million over three-and-a-half years into exascale-focused research and development. Led by the Horizon 2020 program, EuroEXA picks up the banner of a triad of partner projects — ExaNeSt, EcoScale and ExaNoDe — building on their work... Read more…

By Tiffany Trader

Delays, Smoke, Records & Markets – A Candid Conversation with Cray CEO Peter Ungaro

October 5, 2017

Earlier this month, Tom Tabor, publisher of HPCwire and I had a very personal conversation with Cray CEO Peter Ungaro. Cray has been on something of a Cinderell Read more…

By Tiffany Trader & Tom Tabor

Reinders: “AVX-512 May Be a Hidden Gem” in Intel Xeon Scalable Processors

June 29, 2017

Imagine if we could use vector processing on something other than just floating point problems.  Today, GPUs and CPUs work tirelessly to accelerate algorithms Read more…

By James Reinders

Cray Moves to Acquire the Seagate ClusterStor Line

July 28, 2017

This week Cray announced that it is picking up Seagate's ClusterStor HPC storage array business for an undisclosed sum. "In short we're effectively transitioning the bulk of the ClusterStor product line to Cray," said CEO Peter Ungaro. Read more…

By Tiffany Trader

Intel Launches Software Tools to Ease FPGA Programming

September 5, 2017

Field Programmable Gate Arrays (FPGAs) have a reputation for being difficult to program, requiring expertise in specialty languages, like Verilog or VHDL. Easin Read more…

By Tiffany Trader

HPC Chips – A Veritable Smorgasbord?

October 10, 2017

For the first time since AMD's ill-fated launch of Bulldozer the answer to the question, 'Which CPU will be in my next HPC system?' doesn't have to be 'Whichever variety of Intel Xeon E5 they are selling when we procure'. Read more…

By Dairsie Latimer

Flipping the Flops and Reading the Top500 Tea Leaves

November 13, 2017

The 50th edition of the Top500 list, the biannual publication of the world’s fastest supercomputers based on public Linpack benchmarking results, was released Read more…

By Tiffany Trader

IBM Advances Web-based Quantum Programming

September 5, 2017

IBM Research is pairing its Jupyter-based Data Science Experience notebook environment with its cloud-based quantum computer, IBM Q, in hopes of encouraging a new class of entrepreneurial user to solve intractable problems that even exceed the capabilities of the best AI systems. Read more…

By Alex Woodie

Share This