Impact of Apache Hadoop and Big Data Analytics on Banking & Securities Industry
Apache Hadoop and Big Data Analytics are being integrated by the Banking and Securities industry (along with a host of other financial organizations) for analyzing strategies linked with their points of sale, transactions, authorizations, and other processes. These and other relevant data points are helping banks identify, take up, and mitigate fraud. For instance, data collated with the help of Hadoop alerts banking authorities when a debit / credit card is stolen, lost, or used with unusual behavior patterns. The checks in place help the concerned bank create a temporary hold on the specific card while it contacts the owner.
In other words, a bank or service provider has access to relevant data points that showcases the expenditure patterns and transactional behaviors of its clients; thereby having complete information of their likes and dislikes. This leads to the designing of frameworks that make schemes and products suit the need of targeted customers; which in turn increases the demand for an organization’s product and services.
3 V’s—Variety, Volume & Velocity
These 3 V’s don’t necessarily have the same impact ( as cast on other industry verticals) on the processes linked with the banking and securities sector. While investment banks may have been dealing with a very high velocity, and for long periods now, the overall volume remains to be a relatively new factor. It is still emerging as one of the key factors for banks to migrate towards the many benefits of Big Data and Apache Hadoop.
With this in view, banks globally are looking towards solutions for those workloads that are either too expensive, or very difficult to be managed with the help of existing technologies. More so, there are regulatory or legal norms, obligatory capital requirements, and many other risks that impact the working of bank-centric projects. More than 25 percent of such projects are ridden with issues pertaining to rigid data integration, lack of agility, and faster response times with regards to analytics, automation of processes, compliance tasks, and so forth. With stringent capital requirements being an order of the day, IT cost-saving projects are continuing to seek lucrative ways of not only boosting efficiency but also conserving capital.
This is where industry-leading solutions of MapR Distribution and Hadoop come to the fore. They are ideally suited for addressing diverse use cases in this industry. Read on for a closer peep into some of them.
Counterparty/ Client Credit Risk Analytics
Increased regulations and the need for demonstrating risk analysis to bank auditors, so as to avoid facing regulatory fines, guides the sector’s need for calculating client/counterparty risks with credit value adjustments; and in more precise ways. These calculations require lots of storage and intense computational power. So, even as data resides in numerous places and/or is under the ownership of separate trading desks, MapR Distribution tools are making it possible for banks to accumulate all disparate data to offer storage, requisite powers, and the following benefits:
The ability to compute trillions of calculations(credit value) on a per day basis; all on a parallel, cost-effective compute platform
Enabling of easy and fast access to various data sources via distributed, high-performance, NFS storage architecture
Regulations and statutory compliances of the likes of Volcker Rule and Dodd-Frank have led to banks having to keep all relevant data within a central repository. Thereafter, these financial institutions have to invest in analytics for performing stress tests and understanding exposure. Any failure to adhere to these requirements results in steep fines.
Solutions formulated by MapR Distribution allow banks to fall in line with these regulatory compliance objectives and attain the following benefits:
Establish and bring into force an enterprise data hub containing relevant compliance data (unstructured and structured) that is capable of being accessed securely
Perform intensive stress tests with the aid of distributed, high performance computing platforms that consist of fewer servers
Enable Hadoop and Big Data analysts to implement existing visualization and BI tools for developing required dashboards for specific end-user consumption
Real-Time Rogue Detection and Securities Fraud
In the current scenario, there is a stronger need for real time monitoring of trades, especially for the sake of preventing rogue trading. To stay ahead of fraudulent acts such as market manipulation and front-running, banks require the running of expensive and extensive simulations, as well as the implementation of new patterns for handling all rogue activities in real time. By implementing MapR Distribution and other Hadoop based solutions, banks are realizing the following benefits:
Performance of real-time anomaly identification and detection related to known patterns of tasks. Here, modeling and simulations from earlier times come in handy for using learned patterns
Correlation of transaction data and new patterns with other streams like chat, email, etc. This is carried out in a parallel, cost-effective processing environment
Reduction of overall query time on massive volumes of data; usually from long hours to just a few minutes
Construction of a single platform for diverse operational applications/ analytics that leads to further reductions in the overall cost of ownership (TCO)
Flagging of anomalous acts in real time goes a long way in preventing fraud or potential security attacks. MapR Distribution for Hadoop equips banks to construct the usage models for “normal” behavior. Banks can do this by accessing past consumer behavior, analyzing incoming transactions against individuals, aggregating purchasing histories, and taking appropriate actions in case activities fall outside the periphery of normal behavior. With more data being ingested, highly precise and accurate models can be built for segregating suspicious activities from legitimate behavior.
Credit Risk Assessment
Because of the global financial crisis, banks need more accurate ways for determining the credit risk of their clients. Under the circumstances, many quantitative indicators are being put to use for the purposes of credit scoring and credit risk assessment. MapR Distribution for Hadoop is enabling banks to extract customer data in just about every way--from depositing information to sending customer service emails, gaining credit card purchase history, and so forth. With the right tools in place, financial institutions are being able to construct in-depth views of their customers, especially for providing accurate credit analysis and scoring metrics.
Marketing organizations in the banking sector are generally inundated with a vast variety of data. This is due to the explosion of many newer channels linked with social media, web, and mobiles. For offering tailored products/ services, these datasets have to deal with individual consumer behavior and the institutional behavior of larger customers alike. Major banks are now using MapR for understanding institutional client behavior and leveraging the following benefits:
Provision of scalable and robust marketing functions with platforms offering high-speed data and support for multiple tools like Python, Pig, and R
Delivery of upsell/recommendations on specific products to consumers, and at the right time. This is possible through the use of relevant data, click stream, market data, and social graphs
Key Benefits of MapR Distribution and Hadoop for Banking Institutions
MapR appropriately delivers the promises of Hadoop with an enterprise-grade, proven platform that is compatible with a broad set of real-time and mission-critical production uses. These solutions bring in ease-of-use, unprecedented dependability, and record speed to NoSQL, Hadoop, databases and streaming applications—all on unified platforms pertaining to Big Data. Some key benefits include:
Simplified architecture comprising of easy data access for enterprise data, all in a single repository
Responsive, fast access to data that enables real-time operations
Cost-effective storage that brings in the advantages of high-end storage platforms
High uptime for meeting stringent SLAs and avoiding costly downtime
Support operational applications driven by Big Data
Access to high scalability options at low costs, built-in controls for data access, direct data ingestion, existing tools/libraries, volume support, direct Access NFS, integrated security, and so forth
Support for unstructured, semi-structured, and structured data
High performance and complete data protection, along with higher throughput
Apache Hadoop and Biog Data for more Competitive Financial Services
Globally, financial services organizations are experiencing drastic changes. The financial crisis of 2008 had resulted in the bankruptcy and failing of many premium banks; which in turn had impacted jobs, incomes and wealth. Because of this, financial institutions have been working round-the-clock to prevent the repeat of any such crisis in future. In addition, in order to flourish, banks are moving full throttle ahead for detecting fraud more accurately and quickly; thereby, improving upon their operational efficiencies as well as modeling and managing risks, and reducing customer churn.
To this end, banking and financial institutions are turning towards Hadoop and Big Data technologies for reducing risks, analyzing fraud patterns, identifying rogue traders, and targeting marketing campaigns on the basis of customer segmentation--all for improved customer satisfaction.
Go for Big Data and Hadoop training if you are a part of today’s banking and securities industry—it’s important!
Click Here for Big Data Course