Of the top 15 global banks use DataStax
Of the top 15 global banks use DataStax
Transactions per second processed by Capital One
Uptime achieved by ING
Reduced application response time by Allied Payment
Let’s face it: securing financial data in the 21st century is a major headache. There are so many things you need to be thinking about today that you didn’t even have to worry about even five or 10 years ago, such as new compliance laws and regulations, and how to keep sophisticated fraudsters from exploiting all the new touchpoints your customers are using. It all comes down to your database, and here are the specific abilities your database must possess to protect your financial data and keep your enterprise from becoming compromised.
Macquarie Bank uses DataStax Enterprise to help power its digital transformation across a variety of customer-focused channels. The company selected DataStax Enterprise due to its elastic linear scale, tunable consistency and peer-to-peer architecture. Spark was implemented to provide near real time stream processing, as well as in memory distributed computing capability with support for machine learning, and Solr for search and indexing.
Load from electronic banking puts strain on core systems. We capture data changes in mainframe-based core banking systems and replicate them to DSE in real time using automatically generated C++ code. Electronic channels are redirected to DSE using existing legacy APIs. The presentation will discuss solution design, innovative approach to automatic code generation, and lessons learned from the platform implementation.Learn More
Collecting, sorting, and analyzing large volumes of sensitive data is part of every bank and financial services company’s operations. However, how this data is managed is of increasing concern as security regulations become stricter and banks continue to use legacy systems that are quickly becoming outdated. It’s now critical for banks to build a scalable, hybrid cloud database that’s always on and efficiently managing customer data. Here are the top four mistakes the banking and financial services industry (BFSI) is making with its data and effective solutions: 1. Maintaining Multiple Data Silos Vulnerable to Fraud Many financial services institutions still house data in multiple silos. This potentially exposes that data to hackers and compromises security measures. Protecting and monitoring all of this data stored in so many locations can be tricky and can make maintaining legal compliance a challenge. Solution: Using an integrated and unified data collection and storage system easily solves this problem. A dedicated cloud database for banks like DataStax Enterprise centralizes all of this information and greatly reduces complexity. This mitigates risk of fraudulent behavior and help you meet compliance requirements. 2. Having a Limiting Amount and Granularity of Data Collection Many banks implement systems that can store or analyze large amounts of data but a lot of this data is collected in batches—not in real-time—so its relevance is compromised. As a result, banks often end up limiting the data they collect or throwing it away, or, even worse, dumping data into a low value data lake solution where data sits without being accessed for long periods of time. Solution: Banks need to use a scalable, multi-model database and hybrid cloud solution that collects and analyzes data in real time. Collecting data at a granular level can provide more accurate insights into consumer behavior so banks can better target their products and services while providing a more personalized experience. Cloud database and NoSQL database systems like DataStax Enterprise make this possible. 3. Data Collection Across Multiple Systems Many financial services institutions using legacy systems are collecting data across dozens, even hundreds, of systems. This makes it extremely difficult for analysts to derive actionable insights from relevant data because they may not be able to access certain types of data easily and may not have all the data points they truly need. Solution: This is solved with cloud databases for banks like DataStax Enterprise, an integrated system that reduces complexity and facilities the process of collecting actionable data. Extrapolating and analyzing this data can provide a 360-degree view of the customer which in turn helps companies develop AI based solutions and design more personalized, targeted customer experiences. 4. Scheduling Maintenance Windows Customers need bank services around the clock so any interruptions in service because of scheduled maintenance periods greatly impacts the amount of data collected and overall transaction capabilities of the bank. Maintenance windows are simply no longer acceptable to today’s consumer and banks need a system that stays on 24/7. Solution: Investing in DataStax Enterprise, with its always-on architecture, allows for delivering around-the-clock services and coordinating accurate data collection processes. Scheduling maintenance windows that shuts down some or all data collection completely becomes obsolete. Intelligent data management is an important driving force behind the future of financial services companies and banks. Being able to access data in real time, analyze large volumes of data, and produce detailed reports of this data is critical for companies that want to transform the customer experience and maintain regulatory compliance. Using cloud applications designed with always-on architectures can prevent many common mistakes the BFSI industry is making with its data management systems. How Banking Fraud is Changing With Data and AI (eBook) A new era of fraud is upon us, and the best way to fight it is through your database. READ NOWLearn More
The digital age has shaped customers to expect applications to be relevant to them, always available, instantly responsive, and accessible when and where they need them. This “always on, always there, always relevant to each customer” way of thinking has redefined how enterprises do business. The best enterprises and brands are not only focusing on delivering hyper-personalized customer experiences at every touchpoint, they are taking the lead in reconfiguring their organizations to operate around “customer experience.” But with all the focus on customer experience being the new competitive differentiator, there are still areas where companies are missing the mark. These blinds spots can have a damaging impact on the customer experience even if brands are doing everything else right: 1) Customer data is siloed Marketing, customer service, R&D, corporate communications, IT, and sales are still far too often siloed, as far as their data is concerned. Each owns data around the customer, but that data is not centralized with one view of the customer, and that data is not shared between departments. The customer belongs to the brand, but the relationship with the customer is owned by too many disparate functions. Customer data needs to be centralized and shareable to create a 360-degree view. Everyone that touches the customer experience has ownership in the relationship. Like a multidisciplinary healthcare team that provides care for a patient, the customer should feel that they are interacting with a single, integrated, seamless whole, not with separate, non-communicating functions. 2) CSAT is the primary measure of loyalty CSAT surveys provide critical data on customer satisfaction and an important metric to gauge how well the brand is doing on customer experience. But, CSAT scores alone do not equate to loyalty and retention. Today’s customers are loyal to their experiences, not to their brands. They stay with a brand as long as they continue to have the experiences they desire. ThinkJar reports that 67% of customers cite bad experiences as a reason for churn. However, the absence of negative feedback is not a sign of satisfaction. ThinkJar also reported that only one of 26 unhappy customers will complain — the rest simply leave. Which means brands must focus on the unseen, unspoken drivers of loyalty and retention and not rely solely on surveys for an accurate measure. 3) Not every employee is empowered to be a customer experience champion (and they should be) For an enterprise to truly become customer-centric, everyone must personally own the customer experience in the work they do each day. Companies must be able to clearly articulate what defines their standards for customer experience and share it widely from the boardroom on down to IT, sales associates, and influencers. Customer experience education, training, and accountability are essential for everyone if the customer is to have exceptional levels of customer experience replicated each time they interact with the brand. Remember, replication and consistency are what drive retention. 4) Data is not used for real-time insights The ability to deliver hyper-personalization depends on your ability to read, interact with, and respond to customer behavior in real time. If your data platform can’t provide a scalable, flexible foundation on which to build amazing customer experience applications, then your enterprise will not be able to generate customer experiences that keep pace with the demands of the Right-Now Customer. A customer experience-focused data management platform enables real-time personalization that delivers an amazing, tailored customer experience anywhere, on any device, seamlessly across touchpoints. It should scale to handle high volumes and be capable of creating a hyper-personalized, responsive, consistent experience that drives retention and loyalty. Drive consistent customer experience with real-time data management To avoid disruption in the customer experience arena, enterprises must be able to create replicable experiences that are highly personalized to each customer in the moment it matters most to that customer. A consistent customer experience is what builds trust between brands and the customer, and what ultimately drives retention. Data management platforms that deliver in real time are key to building a customer-centric organization in today’s digital age. Becoming a Customer-Centric Organization (eBook)Learn More
19 minute podcast: Martin James, Regional VP of Northern Europe at DataStax, discusses data management trends in the banking and financial services sector and talks about how BFSI players like NYSE and Macquarie Bank have used DataStax to succeed.Learn More
As part of its transformation, including migrating apps to the cloud, Discover Financial Services (DFS) has been exploring solutions using DataStax Enterprise (DSE). In the session Kiran Dronamraju, Principal Engineer and Product Owner for Cloud Data Solutions at DFS, will discuss the DataStax footprint at DFS with a focus on a three-part, secure-fast-reliable concept: Secure: all aspects of access, including shell, user, and transport security Fast: build automation/IaC (Infrastructure-as-Code) for DSE clusters Reliable: DSE’s fault-tolerant architecture and applications We’ll also discuss the operational aspects of the implementation, including monitoring and alerting, before wrapping up the session with a short discussion on upcoming projects.Learn More