Advancing Fintech with In-Memory Computing
Security is of particular importance to payment solution providers. New cybersecurity regulations promote greater data protections, but they also add to performance challenges, requiring service providers to:
- Implement effective security and encryption techniques for user authentication and protection of transaction data
- Comply with complex and evolving security regulations, often in real time
- Access huge amounts of structured and unstructured data, such as historical behavior data that can help predict future fraud
- Identify and stop fraudulent transactions in real time through predictive modeling, machine learning and other techniques
Another key challenge for service providers is successfully mining the data they collect across a wide range of vendors, services, and payment recipients in order to generate insights that can drive new business opportunities.
IMC in Fintech — How is it different
To achieve all this, many payment solution providers are turning to in-memory computing solutions. In-memory analytics mostly rely on built-in chip-based memory for data storage. It is in contrast with those systems that store some or all of the data to be analyzed on a disk. The in-memory computing approach is considered to be faster because it eliminates physical disk reading when querying the data.
In companies that are working with large data sets and complex algorithms, the difference in response time can be several minutes or even hours. If companies are using disk-based systems, long response times effectively force companies to work on relatively small data sets, less complex models. This hampers their opportunity of deriving insights from large data sets. Thus, companies undertaking disk-based approach do not promote agility or fact-based decision-making.
In-memory computing can help in providing an alternative to this option. With in-memory computing, users can work interactively with much larger data sets in more complex models. For example, a financial institution needs to work with detailed projections for unit sales and revenues at the stock-keeping unit (SKU) level. In-memory analytics systems allow them to interactively explore the financial and volume implications of price changes in specific geographic markets or channels.
IMC platforms are relatively easy to deploy. They are inserted between the application and data layers. The data in the underlying RDBMS, NoSQL or Hadoop database also resides in the RAM of the distributed IMC platform cluster, delivering a tremendous performance boost. Extreme scalability is also easy to achieve. Total system RAM can be increased simply by adding new nodes to the cluster. The system automatically rebalances the data across the nodes, adding the processing power and RAM of the new nodes. Today’s IMC platforms also offer the flexibility, interoperability and security payment solution providers need.
Until recently, IMC was considered too expensive because of the high cost of RAM. However, costs have dropped approximately 30 percent per year since the 1960s. Today, memory is still slightly more expensive than disk-based storage, but the increased performance improves ROI significantly. Many payment solution providers and other financial services firms that have implemented an IMC platform have seen a tenfold or more improvement in their ROI.
For instance, Sberbank, the largest bank in Russia and the third largest in Europe recognized the need for a next-generation data-processing platform to handle the expected massive rise in transaction volume and opted for one based on IMC. According to the bank, the IMC platform, which was built using industry-standard hardware, delivered very high performance and reliability while being much less expensive than the technology previously in use.
The three broad advantages of using in-memory computing in finance include:
- Process innovation: In-memory computing facilitates performance gain offers that can give rise to development of innovative applications to differentiate sustainably from competitors.
- Simplification: IMC technology facilitates reduction of layers in these data models, thereby significantly reducing the complexity of these data models.
- Flexibility: Real time calculation of analytical results from raw data, this helps in adding flexibility in terms of two dimensions: Developers can easily change analytical procedures as only a change to a query is required. They can easily plug in new data as an additional source of information because every calculation is started from raw data.
In-memory computing will consequently make its way into the finance industry, bringing better performance and flexibility in the fintech ecosystem.
(References: bobsguide.com, gridgain.com, allerin.com)