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FINANCE

4 ways high-performance analytics powers financial services

4 ways high-performance analytics powers financial services 44

4 ways high-performance analytics powers financial services 45By Helena Schwenk, VP Chief Data & Analytics Office, Exasol

The pandemic only made data-based operations more important for financial services organisations, with businesses needing to make key decisions concerning their real estate footprint, recruitment, investment in technologies, improving customer experience (CX), and keeping in line with regulatory requirements.

These crises draw attention to the importance of high-performance in-memory data platforms given the need for financial institutions to engage with significant data volumes. Having the ability to properly manage and analyse data means they’ll have the context needed for key decisions they can’t afford to get wrong.

Without the right analytics capabilities, financial services organisations risk falling behind in four key areas: fraud detection, customer experience, compliance reporting, and trading analysis.

  1. Accelerated fraud detection to reduce losses

Fraud detection entails analysis of significant data volumes that are diverse and disconnected to identify suspicious activity. Online fraudsters have proven to be malicious and adaptable, which means firms need to integrate more and more data when conducting analysis to keep up with new iterations of their schemes.

Dealing with these increasing data volumes means firms need to embrace machine learning (ML), with complex analysis used to test various scenarios to discern recurring patterns, and importantly outliers. In-memory analytics expedites this process, streamlining fraud detection with immediate insights, with suspicious activity made more easily identifiable at high speeds.

This means firms will be enabled to detect fraud at a faster pace, while maintaining security and performance, and avoiding losses and improving the customer experience, because standard transactions are processed quicker.

For example, an online payment provider could use AI-powered algorithms to identify irregular activity, legitimate transactions, and ensure higher conversion rates. Using in-memory analytics accelerates this, reducing the time required to make decisions on issues like fraud down to milliseconds.

  1. Faster insights for better CX

Firms are competing to offer better experiences to customers, particularly with personalised offers that match their preferences. This occurs in various forms such as providing tailored quotes or providing finance options that meet the current needs of account holders.

Personalisation done well stands to boost retention, reduce churns rates, and improve the bottom line. That said, realising this means firms need to convert raw data into insights that provide access to a 360-customer view.

However, accessing this level of insight means wrestling with significant data stores, with the need to centralise, process, and analyse billions of records from several sources.

CX tasks like customer service reporting or churn prediction are streamlined when using in-memory distributed models, with performance improved for business users by supporting high concurrency if multiple agents are handling customer queries at the same time.

These capabilities give firms the ability to access customer insights at an increased pace, allowing them to meet customer expectations more effectively, whether that’s providing a better service, identifying new business opportunities, or providing personalised outreach when suggesting new services.

  1. On-time, on-budget compliance reports

Financial services firms are constantly expected to meet an ever-proliferating range of regulations, including Dodd-Frank, Solvency II, and CCAR, which require them to give accurate risk assessments. They also need to pressure test against larger volumes of internal and external data, plus meet multiple deadlines, whether monthly, quarterly, or annually. What’s more, noncompliance can mean losing customers, PR crises, fines, and prison sentences.

The risk is too great for firms relying on legacy systems when producing compliance reports. An in-memory data platform is important to navigate these challenges and reduce the time needed to report. This extends to ad hoc queries, allowing auditors to get the answers they need in a time efficient manner for example.

Regulations also often mandate that firms store any sensitive data locally, but report on all data stored on-premises and/or in the cloud. To ease reporting when using hybrid architectures, in-memory databases leverage data virtualisation to access information wherever it resides without the need to extract, load and transfer data – decreasing the likelihood security breaches.

As an example, health insurance firm Siemens-Betriebskrankenkasse (SBK) centralised its data into one in-memory database. This has cut down query run times from six and a half hours to just 21 minutes.

  1. High speed trading for better investment

Investors need access to data in real time to inform their decisions and facilitate better returns. Even minor latency in analytics delivery could result in material losses, and larger volumes of diverse data have become too great a burden for legacy platforms.

Successful trading analysis means assessing various forms of information, including market data, media outlets, and financial data throughout the trading process. Flexible, in-memory databases improve the efficiency of this important trade analysis, including transaction cost analysis (TCA) and advanced algorithms for analysis in real time.

Delivering faster insights via these platforms will empower investors to make more efficient decisions, without the need to wait on reports.

Upgrading your analytics

Success within financial services requires agile decision making, facilitating reduced time to insights. Firms need to capitalise on their data by giving a boost to their analytics capabilities, supporting better analysis for higher profits and greater value to customers. Achieving this requires access to the right data infrastructures to handle important, time-sensitive workloads.

Financial service firms must be able to adapt to any challenges thrown their way. To do so, it’s vital that the sector moves towards modern in-memory platforms with the high performance, scalability, and flexibility to deliver the best results. These capabilities will allow them to remain agile in periods of uncertainty, maintain effective operations regardless – and stay ahead in any situation.

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