4 reasons why finance needs high-performance analytics

Helena Schwenk, VP Chief Data and Analytics Office at Exasol

 

Data analytics platforms are central to the operations of financial services firms. Whether it’s transforming core business processes, shaping customer experience, or adhering to compliance regulations, the ability to capture and interrogate data provides the context for the business-critical decisions that firms need to get right.

The power to leverage data is even more important given the impact of the pandemic, with businesses making significant decisions about the level of real estate, staffing, and technological investment that they need in a post-pandemic work environment. Crises like these highlight the need for firms to be able to rely on high-performance in-memory platforms that offer speed, scalability, and flexibility regardless of where organisations are on their cloud journey.

In financial services, a sector that’s so inherently data-intensive, this is more important still. Without high-performing analytics, there are four key initiatives that risk being left by the wayside: fraud detection, customer experience, compliance reporting, and trading analysis.

 

  1. Accelerating fraud analytics to minimise losses

Fraud detection requires analysis of large, diverse, and disconnected data volumes to identify suspicious activity or trends at a faster pace. This is especially challenging given the changing nature of fraudster’s schemes, with businesses needing to integrate more data into analysis to keep up with new incidents.

Grappling with these increasing masses of data requires machine learning (ML), running complex analysis to test multiple scenarios and identify repeating patterns – or, indeed, outliers. In-memory analytics accelerates this process, supercharging fraud detection with immediate insights to identify irregular sequences and unknown fraud schemes.

As such, financial firms can detect fraud faster than ever before without compromising on security or performance, avoiding losses and keeping customer satisfaction high by authenticating legitimate transactions quickly.

For instance, fintech provider Iyzico, who provide online payment services, use AI-powered fraud detection algorithms to identify suspicious transactions, as well as protect legitimate customers and keep high conversion rates. By leveraging in-memory analytics to run queries faster, they meet their goal of making fraud decisions on online shopping transactions in under 50 milliseconds.

 

  1. Boosting CX with instant insights

Firms are racing to improve customer experiences with personalised offers that make consumers feel recognised as individuals. That could mean insurers providing tailored policies and quotes, or banks offering finance options that match the needs of account holders.

Getting this right boosts retention rates, reduces churn, and ultimately secures the bottom line. To achieve it, financial services firms need to convert raw data into actionable insights that’ll grant them the coveted 360-degree customer view to understand their customers as people.

As a result, firms are wrestling with vast stores of customer data, attempting to integrate, process, and analyse billions of records from multiple sources. Whether it’s customer service reporting, or churn prediction, in-memory distributed architectures ensure optimal analytics performance for many users by supporting high concurrency.

This gives firms accelerated time to customer insights, which enables a better customer experience. The quicker a firm can access insights, the faster it can serve its customers and meet their expectations, allowing them to address customer issues, identify opportunities for new business, and provide personalised communication when recommending services.

 

  1. Ensuring on-time, on-budget compliance reporting

Banks and insurers are under constant pressure to meet new and existing regulatory standards. Regulations like Dodd-Frank, Solvency II, and CCAR require firms to give accurate risk reports and conduct stress tests against large volumes of internal and external data, and meet multiple deadlines on a quarterly and annual basis. Noncompliance means loss of customers, PR issues, hefty fines, and even prison sentences.

These risks are too great for firms to rely upon underperforming, costly data warehouses for compliance reporting. That’s why in-memory MPP architectures are so crucial, expediting the entire regulatory reporting process from data ingestion to report creation and auditing. Firms can not only reduce the time for reporting to meet the stringent deadlines, but they can also speed up ad hoc query performance to answer auditor’s questions in a timely manner.

Many regulations require financial firms to keep sensitive data on-premises, and report on data stored both in the cloud and in-house. To optimise reporting within hybrid cloud environments, an in-memory database leverages data virtualisation to access data anywhere it resides without moving it, eliminating costly extract, transform and load (ETL) processes and reducing the risk of a data breach.

For example, Siemens-Betriebskrankenkasse (SBK), a health insurance company, consolidated its complex data sources – including compliance data – into a single warehouse. This has cut down data query delivery time from six and a half hours to 21 minutes while ensuring data privacy.

 

  1. Unlocking high-speed trading analysis for better investment

Investors need near-instant data to inform their decisions and generate higher returns. Minor delays in analytics delivery could mean material losses. The sheer volume, variety and velocity of the data is not suited to legacy data warehouse systems. Successful trade analysis means evaluating large amounts of data from diverse sources, including market data, media channels, and information from financial data providers throughout the trading process.

A highly scalable, in-memory analytics data warehouse can boost the performance of this important trade analysis, such as transaction cost analysis (TCA) and advanced algorithms power real-time financial analysis. These faster insights will enable investors to make better, more agile decisions at any time, without the need to wait for reports.

 

It’s time for an analytics upgrade

Thriving in finance today requires a greater level of agility. Firms need to turn their data into opportunity by upgrading their analytics capabilities to support real-time decision making, increasing profitability and delivering value to customers. That means having the right data infrastructure to handle mission-critical, time-sensitive workloads.

The pandemic won’t be the last crisis to impact the sector, which means firms need to move away from legacy data storage solutions and embrace new platforms that support high performance, scalability, and flexibility at their foundation. These capabilities will allow institutions to remain agile during difficult times, maintain steady operations during more buoyant conditions – and stay ahead regardless of the circumstances.

 

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