Most of us know this that the volume of data generated and handled in the banking and financial sector is just huge actually gigantic. Most of us using ATM these days, have we ever wonder, from any corner of the world, when you enter your card into an ATM machine, it knows how much limit you have on that card / how much balance you have to transact, etc or you walk-in in a bank branch and submit a request there, the bank executive going to process your request digitally and store the same on the banking systems. Customer interactions with banks have become mostly online, Thanks to big data analytics, as the number of electronic records grows and it is growing at very good peace. This is actually creating seriously a huge and complex data, in order to grow, the banking systems actually started using these datasets, sometime individually and in some case collaboratively, as in with it subsidiary or pear organization in the field. The financial and banking service providers are actively using it to store data, derive business insights and improve scalability. The innovative use of technology in the design and delivery of financial services and products has led to Financial technology altogether. Today most of the banking and financial service providers provides applications include making online transactions and providing better solutions for investment management.
Accenture released a recent report that confirmed that investment in fintech (financial technology) was more than $12 billion in FY15, worldwide. Even the Gartner research confirmed that about 64% of financial service companies used big data in 2017 and the numbers have been soaring ever since. Following the Great Recession of 2008 that drastically affected banks across the world, big data analytics has enjoyed a newfound popularity in the financial sector. When banks began to digitize processes, they also needed to ensure means to analyse massive data with the new age technologies. In the banking landscape today, as customers are increasingly gravitating towards online modes of banking, traditional banking methods are under the threat of becoming obsolete.
Apart from giving easy and fast service via the new age technologies, there are many other business advantages of using these tools to not just managing but designing the new financial products and services. Below are the top advantages of big data in banking:
Efficient Risk Management to Prevent Errors and Frauds
Business intelligence (BI) tools are capable of identifying potential risks associated with money lending processes in banks. With the help of big data analytics, banks can analyse the market trends and decide on lowering or increasing interest rates for different individuals across various regions.
Data entry errors from manual forms can be reduced to a minimum as big data point out anomalies in customer data too. With fraud detection algorithms, customers who have poor credit scores can be identified so that banks don’t loan money to them. Yet another big application in banking is limiting the incidences of fraudulent or dubious transactions that could promote anti-social activities or terrorism.
Provides Personalized Banking Solutions to Customers
Big data analytics can aid banks in understanding customer behaviour based on the inputs received from their investment patterns, shopping trends, motivation to invest and personal or financial backgrounds. This data plays a crucial role in winning customer loyalty by designing personalized banking solutions for them. This leads to a symbiotic relationship between banks and customers. Customized banking solutions can greatly maximize lead generation too.
Easier Filing of Regulatory Compliances
A majority of bank employees claim that ensuring banking services meet all the regulatory compliance criteria set by the Government 68% of bank employees say that their biggest concern in banking services is
BI tools can help analyse and keep track of all the regulatory requirements by going through each individual application from the customers for accurate validation.
Boosts Overall Performance
With performance analytics, employee performance can be assessed whether or not they have achieved the monthly/quarterly/yearly targets. Based on the figures derived from current sales of employees, big data analytics can determine ways to help them scale better. Even banking services as a whole can be checked to know what works and what doesn’t.
Effective Customer Feedback Analysis
Bank’s customer support centres will have a lot of inquiries and feedback generation on a regular basis. Even social media platforms serve as a sounding board for customer experiences today. Big Data tools can aid in sifting through high volumes of data and respond to each of them adequately and swiftly. Customers who feel that their banks value their feedback promptly will remain loyal to the brand.
Ultimately, banks that don’t evolve and ride the big data wave will not only get left behind but also become obsolete. Adopting Big Data analytics and other hi-tech tools to transform existing banking sector will play a significant role in determining the longevity of banks in the digital age. You can ride the big data wave of the future by enrolling for the different Big Data Certification Course. Following are the market leader in Big Data Analytics tools in the current market.
So as you are ready to get trained, let move forward to see on which niche field you should be choosing, are you want to be in business analytics, driving decisions in the business world, or would you prefer the technical challenges of a data scientist doing advanced analytics? To give you some perspective, McKinsey Global Institute’s report on big data predicts that by 2020, there will be a shortage of 1.6 million analysts/managers who can make data-driven decisions versus 140,000-190,000 positions open for data scientists.
Following are the market leaders when it comes to big data analytics, this
IBM SPSS and Big Data
Microsoft SQL based analytics
Dell EMC Data Science certification
A business analytics professional/manager will need a basic understanding of analytical techniques that most data professionals can learn quickly. Analysts/managers spend more time interfacing with people than computers and are often working with broader business questions that can be solved using simpler analytics techniques.