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Technology in Financial Services: Analytics Stack Performance

Financial services organizations operate in a challenging environment. Their industry is one of the most regulated in the world, and their sites, services and applications serve a critical function within the global economy. New technology in financial services is constantly emerging, aimed at helping enterprises conduct their affairs smoothly, compliantly, and free from technical error.

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What is Analytics Stack Performance?

Savvy companies keep abreast of the latest technology in financial services in an effort to keep up with competitors. Everyone wants their applications to be highly available and performance-optimized while generating investor and shareholder returns. Because data-driven analytics are key to the current and future competitiveness of financial services companies, most technology innovation in financial services is focussed on leveraging data to increase uptime and efficiency.

Analytics stack performance is a key example of new technology in financial services. Proactively monitoring the performance of your critical applications and services with big data analytics stack performance can help you avoid operational nightmares and enable you to find and fix application and infrastructure issues before they impact your organization.

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Seven Ways Analytics Stack Performance Helps Financial Services Companies

As a flexible piece of financial technology, analytics stack performance can refine and boost a range of financial services’ company goals:

    • Predicting the risk of churn for individual customers and recommending proactive retention strategies to improve customer loyalty.
    • Providing early warning predictions using liability analysis to recognize  potential exposures prior to default. As a new technology in financial services, analytics stack performance encourages proactive engagement with customers to manage their liabilities and limit exposure.
    • Predicting risk of loan delinquency and recommending proactive maintenance strategies by segmenting delinquent borrowers and identifying “self-cure” customers. A better functioning big data analytics engine enables financial institutions and banks to better tailor collection strategies and improve on-time payment rates.
    • Detecting financial crime such as fraud, money laundering, or counter-terrorism financing activities by pinpointing transaction anomalies or suspicious activities through big data analytics derived from transactional, customer, black-list, and geospatial data.
    • Predicting operational demand based on historical data and future events. Insights from analysis and projections of data allow banks to anticipate call center traffic volumes or predict demand for cash at ATMs.
    • Evaluating customer credit risk by analyzing application and customer data. With analytics stack performance, financial institutions can get better at automating real-time credit decisions based on information such as age, income, address, guarantor, loan size, job experience, rating, and transaction history.
    • Managing customer complaints by using information from various interaction channels helps financial institutions understand why customers complain, identify dissatisfied customers, and find the root causes of problems.
    • The applications and workloads that the Pepperdata analytics stack performance solution can optimize provides the “source of truth” that ultimately underlies a whole range of customer-facing, transactional use cases.

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Analytics Stack Performance = Scalability for Massive Deployments

Pepper data analytics stack performance solutions provide the scalability that makes them the choice of the world’s largest financial services organizations, with some customers running in excess of 1,000 nodes in their distributed computing environment.

Customers with high node counts face unique operational challenges while operating their financial technology, including extremely high numbers of concurrent queries. They cannot afford any service or data loss. To reduce risks associated with potential downtime and data loss, some organizations have established data centers with triple-redundancy cluster architectures.

Financial services organizations with such huge physical infrastructure investments naturally want to maximize their workloads and utilize their infrastructure as efficiently as possible. Pepperdata is a technology innovation in financial services that can create:

  • 90% capacity utilization without manual application tuning
  • Up to 50% improvement in throughput that results in significant savings in infrastructure spend
  • 95% reduction in MTTR, with an average 5,200 hours per year saved on triage and troubleshooting time

For example, a Fortune 100 financial services giant gained control over their runaway data infrastructure spend with the help of Pepper data analytics stack performance products. Learn more about how they did it by downloading the case study here.

Bridging the DevOps Communication Gap

Financial services companies using Pepperdata appreciate being able to bridge the communication gap that exists between developers and IT operations (a gap that can negatively impact application development and the production workloads).

Our new technology in financial services helps across the board. Using Pepperdata Application Spotlight, customers can readily monitor an application as it transitions through the development cycle—from pre-product to production. As the application evolves, issues like bottlenecks, CPU, and memory issues are quickly detected and resolved using Pepperdata Platform Spotlight and Capacity Optimizer to ensure optimal performance in the production environment. Pepperdata Query Spotlight then gives you insights on the thousands of concurrent queries and helps you understand query execution and database performance.

Better communication allows ITOps to help the application team efficiently work through the development transitions. These benefits optimize application performance and uptime and help ensure that SLAs are met.