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!

‘Business Value’ of AI Heads Toward $4 Trillion

April 26, 2018

The rise of AI is reflected in recent market forecasts that predict it will help enterprises develop new products and services around applications like automated decision making. Market analyst Gartner Inc. forecasts Read more…

By George Leopold

Former AMD Chip Chief and ‘Zen’ Architect Jim Keller Joins Intel

April 26, 2018

Intel announced today it has hired top microprocessor architect Jim Keller as senior vice president to lead the company’s silicon engineering group, focusing on system-on-chip (SoC) development and integration. Read more…

By Tiffany Trader

Rackspace Is Latest to Roll Bare Metal Service

April 26, 2018

Rackspace is expanding its managed private cloud services with the addition of six new bare metal instances that it collectively refers to as bare metal as a service. The private cloud vendor announced the new managed Read more…

By George Leopold

HPE Extreme Performance Solutions

Hybrid HPC is Speeding Time to Insight and Revolutionizing Medicine

High performance computing (HPC) is a key driver of success in many verticals today, and health and life science industries are extensively leveraging these capabilities. Read more…

Google Charts Two-Dimensional Quantum Course

April 26, 2018

Quantum error correction, essential for achieving universal fault-tolerant quantum computation, is one of the main challenges of the quantum computing field and it’s top of mind for Google’s John Martinis. At a presentation last week at the HPC User Forum in Tucson, Martinis, one of the world's foremost experts in quantum computing, emphasized... Read more…

By Tiffany Trader

Google Charts Two-Dimensional Quantum Course

April 26, 2018

Quantum error correction, essential for achieving universal fault-tolerant quantum computation, is one of the main challenges of the quantum computing field and it’s top of mind for Google’s John Martinis. At a presentation last week at the HPC User Forum in Tucson, Martinis, one of the world's foremost experts in quantum computing, emphasized... Read more…

By Tiffany Trader

Affordable Optical Technology Needed Says HPE’s Daley

April 26, 2018

While not new, the challenges presented by computer cabling/PCB circuit routing design – cost, performance, space requirements, and power management – have Read more…

By John Russell

AI-Focused ‘Genius’ Supercomputer Installed at KU Leuven

April 24, 2018

Hewlett Packard Enterprise has deployed a new approximately half-petaflops supercomputer, named Genius, at Flemish research university KU Leuven. The system is Read more…

By Tiffany Trader

Cray Rolls Out AMD-Based CS500; More to Follow?

April 18, 2018

Cray was the latest OEM to bring AMD back into the fold with introduction today of a CS500 option based on AMD’s Epyc processor line. The move follows Cray’ Read more…

By John Russell

IBM: Software Ecosystem for OpenPOWER is Ready for Prime Time

April 16, 2018

With key pieces of the IBM/OpenPOWER versus Intel/x86 gambit settling into place – e.g., the arrival of Power9 chips and Power9-based systems, hyperscaler sup Read more…

By John Russell

US Plans $1.8 Billion Spend on DOE Exascale Supercomputing

April 11, 2018

On Monday, the United States Department of Energy announced its intention to procure up to three exascale supercomputers at a cost of up to $1.8 billion with th Read more…

By Tiffany Trader

Cloud-Readiness and Looking Beyond Application Scaling

April 11, 2018

There are two aspects to consider when determining if an application is suitable for running in the cloud. The first, which we will discuss here under the title Read more…

By Chris Downing

Transitioning from Big Data to Discovery: Data Management as a Keystone Analytics Strategy

April 9, 2018

The past 10-15 years has seen a stark rise in the density, size, and diversity of scientific data being generated in every scientific discipline in the world. Key among the sciences has been the explosion of laboratory technologies that generate large amounts of data in life-sciences and healthcare research. Large amounts of data are now being stored in very large storage name spaces, with little to no organization and a general unease about how to approach analyzing it. Read more…

By Ari Berman, BioTeam, Inc.

Inventor Claims to Have Solved Floating Point Error Problem

January 17, 2018

"The decades-old floating point error problem has been solved," proclaims a press release from inventor Alan Jorgensen. The computer scientist has filed for and Read more…

By Tiffany Trader

Researchers Measure Impact of ‘Meltdown’ and ‘Spectre’ Patches on HPC Workloads

January 17, 2018

Computer scientists from the Center for Computational Research, State University of New York (SUNY), University at Buffalo have examined the effect of Meltdown Read more…

By Tiffany Trader

How the Cloud Is Falling Short for HPC

March 15, 2018

The last couple of years have seen cloud computing gradually build some legitimacy within the HPC world, but still the HPC industry lies far behind enterprise I Read more…

By Chris Downing

Russian Nuclear Engineers Caught Cryptomining on Lab Supercomputer

February 12, 2018

Nuclear scientists working at the All-Russian Research Institute of Experimental Physics (RFNC-VNIIEF) have been arrested for using lab supercomputing resources to mine crypto-currency, according to a report in Russia’s Interfax News Agency. Read more…

By Tiffany Trader

Chip Flaws ‘Meltdown’ and ‘Spectre’ Loom Large

January 4, 2018

The HPC and wider tech community have been abuzz this week over the discovery of critical design flaws that impact virtually all contemporary microprocessors. T Read more…

By Tiffany Trader

How Meltdown and Spectre Patches Will Affect HPC Workloads

January 10, 2018

There have been claims that the fixes for the Meltdown and Spectre security vulnerabilities, named the KPTI (aka KAISER) patches, are going to affect applicatio Read more…

By Rosemary Francis

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

Deep Learning at 15 PFlops Enables Training for Extreme Weather Identification at Scale

March 19, 2018

Petaflop per second deep learning training performance on the NERSC (National Energy Research Scientific Computing Center) Cori supercomputer has given climate Read more…

By Rob Farber

Leading Solution Providers

Lenovo Unveils Warm Water Cooled ThinkSystem SD650 in Rampup to LRZ Install

February 22, 2018

This week Lenovo took the wraps off the ThinkSystem SD650 high-density server with third-generation direct water cooling technology developed in tandem with par Read more…

By Tiffany Trader

Fast Forward: Five HPC Predictions for 2018

December 21, 2017

What’s on your list of high (and low) lights for 2017? Volta 100’s arrival on the heels of the P100? Appearance, albeit late in the year, of IBM’s Power9? Read more…

By John Russell

AI Cloud Competition Heats Up: Google’s TPUs, Amazon Building AI Chip

February 12, 2018

Competition in the white hot AI (and public cloud) market pits Google against Amazon this week, with Google offering AI hardware on its cloud platform intended Read more…

By Doug Black

HPC and AI – Two Communities Same Future

January 25, 2018

According to Al Gara (Intel Fellow, Data Center Group), high performance computing and artificial intelligence will increasingly intertwine as we transition to Read more…

By Rob Farber

US Plans $1.8 Billion Spend on DOE Exascale Supercomputing

April 11, 2018

On Monday, the United States Department of Energy announced its intention to procure up to three exascale supercomputers at a cost of up to $1.8 billion with th Read more…

By Tiffany Trader

New Blueprint for Converging HPC, Big Data

January 18, 2018

After five annual workshops on Big Data and Extreme-Scale Computing (BDEC), a group of international HPC heavyweights including Jack Dongarra (University of Te Read more…

By John Russell

Momentum Builds for US Exascale

January 9, 2018

2018 looks to be a great year for the U.S. exascale program. The last several months of 2017 revealed a number of important developments that help put the U.S. Read more…

By Alex R. Larzelere

Google Chases Quantum Supremacy with 72-Qubit Processor

March 7, 2018

Google pulled ahead of the pack this week in the race toward "quantum supremacy," with the introduction of a new 72-qubit quantum processor called Bristlecone. Read more…

By Tiffany Trader

  • arrow
  • Click Here for More Headlines
  • arrow
Share This