Big Data & Real-time Analytics in Finance

The financial sector is especially vulnerable to risk management issues that are time-sensitive. In cases of sophisticated electronic fraud for example, an unsophisticated algorithm or post-hoc system may highlight the incident after the window period to prevent or reverse the damage has passed. Here we look at how the DataReady platform can mitigate or prevent these issues in real-time.

The system can identify patterns in data that indicate where fraud is most likely to occur.

Predictive and Preventative Applications There are a number of concrete scenarios in which DataReady’s real-time analytics can be applied to predict, detect and in some instances prevent unwanted situations.


  • The same ATM card is used within 10 minutes in two separate locations, triggering a high-probability pattern suggesting card fraud. The second location is immediately supplemented with additional verification measures, potentially preventing loss of revenue to the bank.


  • The system can identify patterns in data that indicate where fraud is most likely to occur, allowing for extra countermeasures to be applied only where they are most needed and would therefore be most cost-effective.


  • Policy and governance issues are also amenable to the technology. For example, real-time analytics can detect data compliance violations and have them attended to before they become a headache or source of additional cost further down the line.


  • Other forms of debit and credit card fraud can also be prevented, either by the detection of novel data patterns that require investigation or by matching known fraud patterns to real-time data streams.


Enhancing Inbranch Customer Experience


  • Using in-branch transaction analytics it is possible to identify user usage patterns in real-time. Is a client repeatedly visiting a branch to pay his or her bills? The system can pinpoint such behavior and redirect that client to a more convenient method of transacting, which simultaneously has a better profit margin. The bank clerk can receive a message from the system directing them to discuss alternative channels the client might use to complete these habitual transactions using more suitable or convenient channels. This makes both ends of each transaction more efficient and leads to a better bottom line and a happier client base.


  • Promote Cross channel process and completions.- eg Starting a mortgage application on the web completing in the branch with the Adviser.

The DataReady platform can also be applied to direct revenue generation and improvements to profitability by highlighting new opportunities and increasing efficiency.


  • Imagine that another bank’s client frequently visits your ATM to withdraw cash. DataReady’s analytics system can present such a client with the option of changing banks easily through the ATM interface, perhaps with a personalized and targeted offer. Thereby turning a concession to a competitor into a conversion opportunity.


  • Global machine usage statistics for the network of ATMs can be requested in real-time. This information could be used, for instance, in precisely determining the pattern of cash refills and maintenance. This way resources are not wasted maintaining assets before it is necessary or alternatively neglecting high-use areas where more maintenance is needed to prevent failure
Case Study #1
Financial Use Case #1

International Bank

Wire transfers have a three day window to cleared and deposited at other financial institutions. Internal bank transfer are less complex, one involving millions of dollars took more than six days to clear.



  • Internal Bank Wire transfer took six days?
  • 12% of transfers breach the 3 day clearance threshold
  • Trace process a nightmare for customers



  • Big Data and Real Time solutions provide view across worldwide systems to validate business process



  • Wire transfers can now be validated in seconds instead of days.


Business value:

  1. Wire transfers now validated instantaneously.
  2. Manual process automated, optimizing precious IT resources.
  3. Bank can now process structured and unstructured data.
  4. Technology to be extended to other lines of business.
Case Study #2

Financial Use Case #2 



Bank could not manage Service Level Agreement (SLA) with ATM cash replenishment service Provider



  • Out of service ATM’s not good for any bank..
  • Cash Management and opportunity cost
  • Could not measure P&L of individual ATM
  • Weekly / batch aggregate reports not providing timely ATM key performance indicators?



  • Implemented real time ATM  transaction data analysis solution



  • Big Data with true Real Time functionality holds service providers to SLA


Business benefits:

  1. P&L for each individual ATM could now be measured
  2. Opportunity cost negated and cash management optimization per machine.
  3. Real time event management and cash replenishments.
  4. Great customer service experience.
Case Study #3

Financial Use Case #3


Property & Casualty Insurance Company had no way to proactively limit its exposure to ever increasing flood claims. Each flooded basement cost them on average $16000 to repair.  Industry risk exposure is over 2 billion per year.



  • No way to limit exposure to flood claims or predict with any certainty when torrential rain might wreak its havoc on unsuspecting homeowners



  • Installation of innovative water sensor technology with ability to block and prevent server back ups in your basement connected to Environment Canada weather service  integrated to Real Time analytic platform which triggers deployment of server blocking balloon during rainstorms, local sewer back up, catastrophic weather events


  • Identify problem areas and charge accordance with increased exposure.
  • Cost saving per occurrence $16000
  • Incenting customers to adopt Home Flood protection to further limit risk exposure


Business benefits

  • Competitive advantage and game changer in the Industry
  • New revenue stream for Insurance company

Increase customer satisfaction – priceless