Data Science: a blessing for today’s banking world
Standing in long bank queues is a thing of the past. Today, banks are no farther than a click on our mobiles or laptops. With the advent of digitalization, everything transited to the online world. To serve customers where they reside, Banks also moved to online platforms.
Traditional banking believed to have lesser risks, today, seems inappropriate due to obvious reasons. Therefore, banks try to develop secure systems online to serve customers with their banking solutions in minimal effort.
But, as Banks involve the motion of money, there lies a great great deal of risk by transiting to online banking platforms. Cybercrimes, frauds becoming prevalent in recent times further elevates the risk and scrutiny associated with modernized banking.
But, technology is always here to help. Data Science, a newly emerging field can assist the banking and insurance sector in some magical ways providing reliability, security, trust, and whatnot!
What is Data Science?
Data science is an interdisciplinary arena playing around data. It uses some stable scientific methods, algorithms, processes, and operations to extract and analyze knowledge and critical information from loads and loads of data. This can also assist in applying extracted data to a broad range of applications. Data science can not only extract data but also highlight some crucial points and connections in data that otherwise are impossible to spot.
As banking involves complicated data collections, incorporating data science can provide a stable and convenient way for operability of today’s banking systems.
Here is a list of five amenities provided by data science to today’s modernized banking-
- Customer Data Management
The finance industry relies on data. The massive amount of data about customers from different demographics, transactions, account holders, any unprotected activity or protected activity carried on online platforms, external payment gateways provides some insightful analytics about customers.
The data from millions and millions of customers can play a pivotal role in banking management and can serve as a repository of customers. As banking solely relies on the foundation of the security of customer data, Data science can aid in maintaining and handling large customer data.
Data scientists leveraging data science can process and manage hefty customer data with the aid of specific data science algorithms and operations. This fuels the decision-making ability of the banking system and ultimately elevates profits for banks. Data science can also aid in the management of both structured and unstructured data.
2. Personalized services
A customer always likes to be entertained as an individual. That’s where personalized services make an impression. This can be done by serving them with specific insurance policies, loan reimbursements, discounts etc. But, banks having millions of customers feel cumbersome to execute these personalized services.
Data Science saves the hassle. It is an authentic solution for such problems. With the aid of specifically designed algorithms, data science can enable banks to send customers personalized alerts, offers, facilities, and much more. This has visibly extended the banking sector’s ability to provide customized services and to establish trust among customers.
3. Risk analysis and management
The banking sector is prone to anomalies and risks. But, these risks if managed optimally can be controlled and curbed. The insurance sector; one of the main sectors of banking often gets losses due to loopholes in the system. Thanks to technology, Data science can now assist in mitigating these downfalls.
It can, by the aid of previously collected data, identify the glitches in the system. This enables banks in designing risk-free models of insurance policies and also determining the credit score or worth of their clients. The development of specific data science algorithms can also provide deep insight into the demographic markets thereby helping insurance companies to target specific locations and predicting profit-loss ratios.
4. Real-time analysis
No matter what industry one may consider, real-time analysis always helps. Traditional banking collecting data in real-time always felt limiting when it came to comparing and contrasting the real-time data for immediate implementation of solutions, changes, and amendments.
Modernized banking, however, can conquer these glitches with the aid of Data Science. Data Science integration in the banking world provides real-time analytics that keeps banks updated all the time. It further elevates the data and online portal’s security. It also enhances banking official’s ability to solve customer’s issues right at the moment.
This gains more importance in online payment gateways and transactions. In these systems, real-time analysis saves from massive frauds.
5. Fraud Detection
This is practically one of the most important applications of Data Science in the banking and insurance industry. Curbing and controlling banking frauds at first hand establishes customer’s trust and subsides any possible banking malfunctions.
To prevent fraud, Data science can efficiently identify kinks in the banking systems. This is generally established by creating layers and two-factor authentication on baking systems, strong IPs, detecting servers, etc.
Data Science: a present and future of banking
So far, many banking platforms have leveraged data science for serving customers better. It is also hypothesized that in the coming future, there would be no physical banks and modernized banking would take over traditional banking completely.
In such a possibility, Data Science can be the savior. By end-to-end managing and keeping an eye on crucial data, frauds, risks, and services, Data Science can hold the banking sector at a place. If modernized banking has a future, the fountain would stand on Data science.
Aarsh, Co-Founder & COO, Gravitas AI