Financial Industry

Expanding Digital Banking’s Value Proposition – Use Cases

With the help from big data analytics, banking providers can develop new products and services that are based on a deeper understanding of each customer at the individual level. By collecting huge streams of information flowing from customers’ account activity, banking providers can learn about their consumers and predict their needs by analyzing the wealth of data available. Financial institutions can leverage big data for the kind of insight needed to deliver more personalized products and services, in new ways that are more proactive and in real time.

For example, by examining correlations between the timing of purchases and recognizing that a customer is nearing his credit limit, the banking provider can offer to extend the line of credit by sending the customer an opt-in offer to his smartphone, capturing the customer’s business before he decides to get another credit card.

Another example is offering the customer an online service for an automatic transfer of a specified amount from the account into a higher interest-earning investment option according to a specified schedule. The bank can share its big data insights deriving from  analytics of how other customers have used their online service for their investments, and provide the customer with a data-driven backed up suggestion of an amount the customer can comfortably afford to transfer every month, while helping him/her to make a better, more informed decision.

Combating Risk With Predictive Intelligence

The Financial Industry is constantly flooded with enormous sets of data. Data needs to be captured, stored, and analyzed in order to produce effective risk-management intelligence. SQream Technologies helps financial institutions with petabyte scaling data sets to identify patterns and predict potential future outcomes. With SQream DB organizations are able to mitigate their risk exposure, balance risk with opportunity, and reach more informed decisions based on accurate, predictive analytics. Apart from risk management, SQream DB also addresses challenges related to high frequency trading and algorithmic execution;  financial analytics; fraud; financial processes; predictive analytics for customer  engagement and sales; behavioral data analytics; data security and legal protection.

With SQreams’ predictive analytics tool, financial institutions now have the opportunity to empower themselves with future-looking enterprise-wide visibility to drive transformational change and cut downtime. Financial institutions can now pull in data from all departments, run predictive scenarios, analyze and summarize their data.

DOWNLOAD SQream DB Brochure

DOWNLOAD Finance Case Study

  • Drives market momentum among cutting-edge marketers
  • Increases user profiling insights for targeted marketing and customized subscription plans
  • Delivers rapid, real-time and effective campaign analysis – enabling experimentation, quick adjustments and campaign optimization
  • Automates marketing processes and cross-channel measurements
  • Enables delivery of remarkable customer experience, yielding revenues
  • Leveraged correlations of all network/ communication, InfoSec, endpoints, activity logs and production data, for cyber security analytics
  • Extract and deliver critical actionable real-time knowledge to CISO/ cyber warfare specialists, enabling prevention/ quick remediation of cyber attacks
  • Mitigate cyber security risks caused by mobility, lack of visibility and multiple global interconnected network systems

When information is assessed properly, it offers the financial institute with a new layer of context that provides insights impacting growth-targeted metrics and performance indicators, such as:

  • The largest revenue risk
  • Promotions that peak customer sentiment and loyalty
  • Forecasts that reflect real-world factors that shape the financial institutes’ future