Big Data at an International Financial Services Firm
For one multinational financial services institution, cost savings is not only a business goal, it’s an executive mandate. The bank is historically known for its experimentation with new technologies, but after the financial crisis, it is focused on building its balance sheet and is a bit more conservative with new technologies. The current strategy is to execute well at lower cost, so the bank’s big data plans need to fit into that strategy.
Big Data at Bank of America
Given Bank of America’s large size in assets (over $2.2 trillion in 2012) and customer base (50 million consumers and small businesses), it was arguably in the big data business many years ago. Today the bank is focusing on big data, but with an emphasis on an integrated approach to customers and an integrated organizational structure. It thinks of big data in three different
“buckets”—big transactional data, data about customers, and unstructured data. The primary emphasis is on the first two categories.
Big Data at a Top 5 Property and Casualty Insurer
Started in 1922 by a handful of military officers who offered to insure each other’s vehicles when no one else would, the insurer has become a financial services powerhouse, offering a broad range of insurance and banking services to military members and their families. The size of its customer base and breadth of products make big data a natural next step in the company’s already-advanced technology portfolio.
How a Malaysian bank used data science to increase its credit card spend
Collaborative filtering – a technique to predict user interest based on the behavior of other users – was applied to detect spending patterns. For example, the bank wanted to know how frequently merchant offerings appeared against customers with specific product holdings. And how often merchant offerings showed up against customers of a particular demographic.
China’s fifth largest bank achieves record response rates with event-based marketing system on Teradata
Teradata (NYSE: TDC), the leading analytic data solutions company, announced that the Bank of Communications, China’s fifth largest commercial bank, has deployed its event-based marketing solution on the Teradata Active Enterprise Data Warehouse platform. The solution will enhance the bank’s ability to make the best decision possible based on its enhanced operational performance and effectiveness.
Malaysia Bank Adopts Artificial Intelligence Early Warning System to Reduce Risks of Non-Performing Loans (NPLs)
The client is one of the largest international banks, headquartered in Singapore with a prominent presence in the Asian region. As a Financial Institution with significant focus on commercial loans, the challenge of managing credit risk from loans turning delinquent is pervasive and a major concern to the bank. With a clear understanding that high NPL ratios demand greater loan provisions, which reduces capital resources available for lending and dents the bank profitability, the Bank decided to embark in this journey to mitigate the risks of NPLs.
Singapore Bank Embraces Machine Learning To Boost Their Advertising Campaigns
The client is one of the largest banking and financial services corporations in Asia. Headquartered in Singapore, the conglomerate has operations across multiple countries around the region. The bank has a substantial budget for digital ad campaigns. In the past, however, a large portion of their digital advertisements were not optimized; their ads were reaching many viewers who either had no interest in their products or lacked the propensity to purchase anything from the bank.
European Retail Bank Uses Big Data To Detect And Prevent ATM Fraud
The leading retail bank in Europe uses Striim to detect and prevent ATM fraud and money laundering. The Striim platform analyzes ATM transaction data to monitor and alert “impossible distance” fraud by detecting transactions on multiple ATM devices within a short span of time. It also identifies potential money laundering cases by detecting when there is a maximum ATM withdrawal from one account and transfer the fund to another account, followed by maximum withdrawal from the latter account.
Significantly reduced losses caused by ATM fraud via immediate detection and action, and improved compliance with anti-money laundering laws while minimizing risk exposure.
Achieved real-time visibility into the ATM activities across the country enhancing customer behavior analytics and targeted marketing campaigns.
Easily sets up and modifies applications with multiple pre-defined criteria that indicate various fraudulent behaviors and money laundering activities.
A Leading Credit Card Network Uses Big Data To Increase Alert Accuracy
When its existing SIEM solutions resulted in many alerts and false positives that the security team could not act upon, the leading credit card network turned to Striim to increase alert accuracy with more sophisticated rules, and improve the security team’s understanding of the alerts generated.
Striim ingests and joins security devices’ log and session data files in AVRO format, representing every security-related event from 50+ siloed security applications. With multi-log correlation and advanced pattern matching capabilities it accurately and immediately detects cybersecurity breaches and attacks. Striim’s results are sent to real-time dashboards and are written to data marts.
Detects cybersecurity threats faster and more accurately compared to existing SIEM solutions using an aggregate view into events.
Responds to security threats in real time automatically and continuously updates blacklisted IP addresses for proactive defense.
Improves security analysts’ productivity by providing the cybersecurity events with full context for fast investigation and action.
Not really a case study, but it is surely a case for study
> 5 Big Data Use Cases in Banking and Financial Services
> Big Data jigsaws for Central Banks – the impact of the Swiss franc de-pegging
> Big Data; Using Google Searches To Predict Stock Market Falls