Big Data in Banking
This blog by AICoreSpot explores the role of big data in the banking sector.
Big data is characterized by four primary features: velocity, volume, veracity, and variety.
The international financial services sector produces humongous volumes of structured and unstructured data on a daily basis through the process of billions of financial transactions, in addition to communications via email, audio/video communications, weblogs, call logs, and references on social media.
One key driver of this information explosion is an appreciation in international payment volumes, driven by ecommerce and mobile payments. It is hard to predict how the international COVID-19 pandemic and connected economic limbo we’re currently facing will impact the international payments market, but prior forecasting pegged estimates to get to 2$ trillion midway through the 2020s, with a compound yearly appreciation rate of 7.83%. E-commerce also persists to expand considerably, particularly in a scenario where customers are being forewarned to carry out as minimal in-person shopping as possible, for obvious reasons. ATM utilization, paperless mortgage process, and closing, P2P payments via apps such as Venmo and Cash.app and several other mobile and remote electronic banking services are escalating in favor amongst users.
What are the concrete advantages of big data in banking?
With a plethora of diverse technologies serving as a standardized touchpoint for data access and commerce, banks are putting out and engaging in the consumption of jaw dropping amounts of data. Handling all of this information puts forth a bevy of business and information technology challenges, however, it also invokes avenues for the banking sector to expand their business, tackle fraud, and enhance their operational efficiency.
Through the application of analytics solutions driven by the cloud, AI, ML, and NLP, banks can harness their information for previously unfathomable levels of understanding into every factor of their business operations. They will be able to gain insight on what has transpired historically, why a particular event or sequence of events, or a phenomenon occurred, and predict what is going to happen next, taking into account the aforementioned data. Ultimately, all this dissection will enable them to provide solutions to the next question that follows through natural reasoning, “What do you (and your clients) want to happen?”
Big data use cases in banking
The way in which banks leverage big data hasn’t changed dramatically, they are much the same as when these institutions first came to the realization that they could leverage their humongous information reserves to produce proactive, actionable insights: identifying fraud, enhancing client understanding, streamlining and optimization of transaction processes, optimization of trade execution, and eventually staying relevant in an overcrowded market by providing standout client experiences. As these institutions continue to collect even more volumes of data, the insights which are an outcome of analysis of this data, and client experiences turn more accurate and relevant. Here are a few examples to provide illustrative understanding:
- Western Union provides an omnichannel strategy that customizes client experiences through the process of > 29 transactions each second, followed by integration of all of that information into a singular platform for statistical modeling and predictive analysis.
- A bank in Eastern Europe that was initiated with no physical locations in the late-2000s providing credit cards and other varied banking services, is staying on top of the web offerings of its more mature and more established contestants by leveraging big data analytics to evaluate and respond to credit applications in near real-time – a client pleasing approach that has enhanced conversion rates for specific upsell campaigns 10x.
How are banks tackling the new challenges presented by big data?
Moving from conventional information warehousing to running Hadoop with its largely parallel engine on commodity hardware enabled banks to restrict the time span taken to extrapolate understanding from their data from three months to 24 hours or less. Cloud-based information process minimized that time span even further. Although, banking institutions still have a tendency in processing data as monthly sets, which implies they may not detect a trend for period exceeding thirty days.
Apache Spark is one potential answer to this issue. Just like Hadoop, it’s an open-source big data analytics engine, but its quicker, more scalable, and more accessible. It can also be leveraged natively in cloud settings to obtain and analyze data that is being streamed in real time, for more prompt and correct replies to business queries.
Big Data requires management and governance
Spark and Hadoop can shift massive volumes and variants of information into a data lake so it can subsequently be pumped to an on-premise or cloud data warehouse where it is accessible to enterprise users. Although, they cannot verify or authenticate if the information is fit for usage. Neither Spark, nor Hadoop execute data administration or data governance on a native basis, so they cannot assist enterprise users comprehend what they have on their hands, what the implications are, or how it’s utilized. Further, they do not impart data lineage so users can view all of the transformations their data has underwent on its trajectory from source systems to analytic tools across the organization.
Tech is modular and commoditized
Banks have tackled the costs, gaps, skills, and infrastructure administration challenges of big data analytics by moving data process from on-premises hardware to cloud-based or hosted colocation facilities. Although, when a local credit union and an international bank have similar accessibility to AWS, Microsoft Azure, or a managed service provider, the capacity in processing massive volumes of information is no longer a competitive differentiator.
Banks must develop the capability to act quicker to transform their information into smart understanding, and then put that understanding into practical use to enhance client service, link customers to data and products when and where it’s most required, and safeguard susceptible information and customer accounts from malicious actors.
Enhance data-driven decision making in the banking sector with smart big data management
When everybody has massive amounts of data, what’s most critical is how they leverage that data. Data should be obtained from a plethora of sources, governance and consumption should be facilitated, and this includes detection and obfuscation of sensitive information and distribution based on the access rules.
Through these methods, end-users can have faith that the information they’re evaluating is good, accurate, in compliance, holds relevance, and is secure. Implementation of automated decision-making based on AI, ML, and NLP, functional, real-time, scalable process makes sure that the processes have trustable, secured information, with good governance. Ultimately, whatever decisions your banking institution undertakes, they’re based on solid data.