The Emerging Role Of Data Science Professionals In Banking Sector

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We no longer make payments with checks, visit banks to deposit money or utilize ATMs to withdraw cash. But thanks to the digital revolution, you may now transact with a single click while sitting at your convenience wherever in the world.

On the other hand, there has been an unwelcome increase in theft, fraud, and crimes. Financial organizations must take significant steps to ensure that customer financial data is secure and unlikely to be misused. They also began employing data science experts, who lightened their load of solving various problems and making difficult choices.

Data Science in Banking

A large amount of data has been gathered as a result of customer transactions, financial activities, purchases, revisions to the financial information, and profile information.
As a result, manual data processing is unable to manage and oversee all of this data. The poor quality of their data alone causes firms to lose around $15 million in revenue, claims Gartner.

Data science has brought many possibilities into the banking industry with measures and tools to monitor, control, and manage vast amounts ...
... of data. With the data science strategy, banks provide the best customer support and build future insights through prediction models and forecasting, risk management, compliance, safety, etc.

What Data Science Professionals do

Data science experts provide numerous benefits to organizations.

To analyze company risks, identify them, and address them
Using precise variables and data sets for analysis
Gathering and separating data from numerous sources
Observing and analyzing the facts to make sure it is pertinent
Using data mining to find trends and patterns
Using statistical and visual evidence to compare and communicate the results Predictive modeling for decision-making

Check out the data science certification course in Canada, to gain profound knowledge on latest big data tools and techniques.

Role of Data Science Professionals in Banking
Fraud Detection and identification

E-commerce transactions have multiplied, and Internet usage has expanded. According to emarketer, with $4 trillion in sales in 2020, retail e-commerce transactions will rise at a rate of roughly 27.6%. But as online transactions increased, so did the number of fraudulent activities.

Professionals in the field of data science utilize machine learning and data science tools to identify and stop fraudulent activity. For instance, Alipay employs Apple's Touch ID or Face ID to validate users' identities as part of its fraud detection methods.

Controlling the customer data
For example, despite holding a substantial market share, a major consumer bank in Asia saw a reduction in its product ratio per client. Fortunately, the bank was successful in locating 15,000 microsegments for their intended clients, according to McKinsey research. Data science and big data were utilized to explore customer information such as transactions, credit card statements, purchase histories, demographic data, transfers, and payments, among other things.

Data science aids banks in the gathering and analysis of massive amounts of data. Furthermore, it aids in decision-making by separating the relevant data from a vast consumer data set and processing it.

Risk Modeling
According to a recent survey, 73% of bankers claimed that the banking sector was experiencing the most obvious surge in risk analytics investments. The utilization of Big Data and Data Science for business solutions is a goal of data science specialists. The market for risk analytics is predicted to reach $65 million by 2026 at this quicker rate.

Data science experts use data science to identify risky assets and give borrowers financial advice rather than manually calculating risk scores. Classifying defaulters and doing credit risk analysis is also simpler for banks.

Customer Lifetime Value(CLV) prediction
Banks constantly require a 360-degree examination of consumers. It is crucial to consider the client's qualities to build a long-lasting and mutually beneficial relationship with the customers.

Data scientists should consider consumer information and use effective customer segmentations to enhance customer interactions. They analyze data, understand customers using decision trees and models, and estimate CLV to choose the best customer retention tactics.

Recommendation engines

Insight-driven enterprises would be valued at $1.2 trillion worldwide by 2020, according to a 2016 Forester forecast. To create algorithms that identify client profiles, prevent the recurrence of offers, and streamline the data filtering process, data science experts employ machine learning and data science technologies.

On the basis of consumer transactions and personal information, recommendation engines are utilized to obtain results and insights.

Skills required for Data Scientists in Banking

According to IBM, the financial and insurance industry makes up 59% of all job requests for Data Science and Analytics roles.

You need both technical and non-technical skills to work in the banking industry as a data science specialist. You first need programming expertise to work with data science and big data. It would be ideal to have a data science certification and in-depth knowledge of data analysis software.

Also preferred is expertise in coding. To swiftly evaluate massive data sets, you also need to be familiar with open-source frameworks. Professionals in the field of data science must have a solid understanding of the analytics engine, streaming modules, machine learning, artificial intelligence, statistical, and data visualization tools.
The non-technical abilities include teamwork, research, decision-making, and communication.

Conclusion
Over 60% of the surveyed enterprises employed more than 50 data scientists between 2021 and 2022. With the data science strategy, the banks excel in many other areas as well, including customer service, security, prediction and forecasting, consumer sentiment analytics, profiling, segmentation, and marketing. In order to give banks and customers the greatest experience possible, data science is essential. If you are someone looking to advance your career, head to Learnbay’s data science course in Canada. Acquire the skills and become an IBM-certified data scientist or analyst.
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