GPU Computing in Financial Analytics- A Hit Or A Miss?

The technological advancements in graphics processing are opening doors of new horizons, especially regarding GPU in finance. Although the concept of graphics cards in finance is a little too technical, there is an exciting story to embark on in the evolution of exchanging banking information. Have a read!

In the initial 1800s reported, the Rothschild banking association, which was in France, set up a vast network of pigeon lofts. All across Europe, a coop of racing pigeons was deployed to carry the latest information between their financial houses. It is also reported that the British win at Waterloo was made known to Nathan Rothschild through pigeons.

And here’s the catch:

When most stock traders expected the British to lose, Nathan got to know the information before anyone and made millions. Back then, pigeons were the fastest mode of sending and receiving information to stay ahead of competitors.

Cut to a few hundred years later, the finance or banking industry is on the front ground to utilize the latest technologies to get that competitive edge.

Brokerage firms, banks, insurance firms, hedge funds, and credit card companies are consistently testing the technologies for their operations to incorporate AI applications and real-time analytics. The main objective of implementing the latest technologies is to eliminate the risks of fraud, perform better trades, and enhance profits.

This aspect of parallel processing in GPUs is the most sought-after feature of the processor. Earlier, the computation required in analytics applications was handled by the CPU. Still, the task could not be processed parallelly because of its sequential processing and a smaller number of cores to work with.

The new analytics applications are being designed to make them compatible to work with the data processing feature of GPU. With over 4,000 processing cores, tasks that are heavy on computation can be easily carried out parallel with GPU.

A few creative horizons presented by GPU databases in the financial industry are discussed below. Have a look!

Assessment of Risk

Whether insurance or stock trading, assessing the risk stands crucial for every financial service, and the calculation of risk score is highly dependent on complex algorithms and massive data sets. The assessment is quite CPU-intensive and is usually carried out in batches over the night.

With GPU, the data aggregation time is reduced from hours to seconds. The processing and sharing of datasets can be executed parallel, and the final results are presented at the CPU level.

This lets the insurance firms deliver the quote over the phone rather than waiting for another day. The portfolio analysts can easily do a risk assessment for a handful of stocks without setting up another meeting.

In addition, the traders can also study the impact of any news on the stocks and proceed in the market accordingly. The financial services relying on speed will leverage a heavy competitive edge by incorporating GPU in their operations.

Also Read: The NVIDIA A2: Leading AI Closer to the Edge

Analysis of Real-Time Sentiment

To keep the trading models informed, the quantitative traders utilize sentiment indicators such as analysis of supply chains, social media, company forums, data from satellites, and news. The datasets which are geo-temporal and text-heavy can be handled by implementing machine learning and NLP methods for optimizing alpha and identifying the opportunities.

Quick support for queries

The vast datasets involved have instant regulatory compliance queries, which are generally slow and tedious. This results in the customers having chances to be forward of any compliance concern while leading to swifter reporting on a large scale.

Reduction in Fraud

In the United States, credit card fraud accounts for over $16.3 billion in a year, and it is hard to calculate the loss incurred by merchants in declining the transactions which otherwise should have been approved. A few of the GPUs in the market can considerably reduce both issues.

One of the main factors driving credit card fraud is that merchants and banks are under pressure to make haste decisions to minimize a customer’s wait time.

But datasets required for risk assessment that have high cardinality are tough to process and index in real-time. GPU databases can quickly scatter the algorithms to various processors and nodes, resulting in fast detection of anomalies, leading to better decisions in less time.

The parallel processing feature in a GPU delivers linear scalability when used for a task, which also improves the quality of decisions.

Also Read: Harness the Power of GPUs to Accelerate Analytics Processing

Personalization in Real-time

The finance graphics card analyzes each and every pointer of customer behavior, which assists the consumer banks in delivering a centered personalization. The customer data is extracted from various sources like social media posts, data of location, transactional data, and customer support records.

The main objective of it is to build loyalty toward the brand by providing customized services and content to the customers. This feature is utilized for selling marketing or up-selling offers while building brand loyalty. The processing power required for processing, quick ingest, and the GPU database gives real-time analysis.

Better and Swifter Trades

When it comes to stock trading, every second counts, the decisions on the computer are made by amalgamating massive historical data while applying mathematical models for comparing the present price trends to the existing ones. Also, in the in-memory databases in a GPU, the trading firms can put all of their data in the memory and carry out the processing parallelly.

Some of the databases in GPU are explicitly made for machine learning which lets the algorithms detect certain anomalies which humans cannot find. The processing of streaming data can also be done smoothly with GPU databases.

All these features combined help the traders apply calculations to real-time pricing, making efficient and better decisions.

Conclusion

The financial industry is amongst the most data-driven initiatives across the world. But the data alone does not have much value unless it is appropriately used and analyzed quickly to achieve the much-needed competitive edge.

GPU in finance and its high-level analytics can assist in generating better revenue for your business and make it reach the finish line before the other pigeons come home to roost.

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How GPU Computing is Improving Financial Analytics GPU vs. CPU for Data Analytics Tasks – Which One is Best? Harness the Power of GPUs to Accelerate Analytics Processing Financial Fraud Detection with Deep Learning and AI