Gone are those days where we used checks for payments, made a deposit through bank visits, or withdrew cash from ATMs. But, the digital transformation has made things easier, where you can sit at your comfort anywhere in this world and transact in a click.
However, the flip side is that theft, fraud, and crimes have increased undesirably. The financial institutions had to adopt serious measures to ensure that the customer's financial data is safe and not prone to misuse. Also, they started hiring data science professionals who simplified their burden of finding solutions to various issues and making complex decisions.
The customer transactions, financial operations, purchases, modifications in the financial information, and their profile information have contributed to a heap of data.
Hence manual data processing cannot manage and monitor this entire data. According to Gartner, organizations incur a financial loss of about 15 million dollars only due to the poor quality of their data.
Data science has brought numerous possibilities into the banking sector with measures to monitor, control, and manage huge data.
With the data science strategy, banks provide the best support to the customers and build future insights through prediction models and forecasting, risk management, compliance, safety, etc.
Data Science Professionals help organizations in many ways:
1. Fraud Detection and identification
Internet usage has increased, and e-commerce transactions also grew multifold. The retail e-commerce transactions saw about a 27.6% growth rate in 2020, with $4 trillion in sales, as per e-marketer. The upsurge in transactions made online, however, gave birth to many fraudulent operations.
Data science professionals use machine learning and data science techniques to detect and prevent fraudulent activities. For instance, Alipay uses fraud detection measures to verify the identity of users with Apples’ Face ID or Touch ID.
2. Controlling the customer data
For example, a leading consumer bank in Asia experienced a decline in its product per customer ratio, though it had an appreciable market share. Fortunately, the bank was able to find 15,000 microsegments for their target customers – McKinsey report says. They used data science and big data to explore customer information like transactions, credit card statements, purchase history, demographic data, transfer, and payments, etc.
From huge data, data science helps banks to collect and analyze them. Additionally, it helps to isolate the necessary data from a huge customer data and process it to improve decision-making.
3. Risk Modeling
As per recent survey, 73 percent of bankers said that the banking industry faced the most evident increase in their risk analytics investments. The data science professionals are making efforts to use Big Data and Data Science for business solutions. At this faster pace, by 2026, it is estimated that the global risk analytics market will reach $65million.
Instead of manually calculating the risk scores, data science professionals use data science to detect hazardous assets and provide financial advice to borrowers. Also, banks find it easier to analyze the credit risks and classify their defaulters.
4. Customer Lifetime Value(CLV) prediction
Banks always require a 360-degree analysis of customers. For maintaining a long-term and mutually beneficial relationship with the customers, it is important to consider the customer characteristics.
Data science professionals must consider customer data and pamper customer relations with effective customer segmentations. With decision trees, models and analytics, they understand customers and use CLV prediction to determine the customer retention strategies.
5. Recommendation engines
In 2016, Forrester predicted that insight-driven businesses would be collectively worth $1.2 trillion by the year 2020. Data Science experts use Machine Learning and Data Science tools to build algorithms that identify the customers’ profiles, avoid the repetition of offers, and simplify the data filtering process.
Recommendation engines are used to get the results and insights based on customer transactions and personal information.
6. Predictive and real-time analytics
About 90 percent of the top 50 banks worldwide use advanced analytics to predict future events. Real-time analytics enable the customers to understand what hinders a business. With predictive analytics, customers can choose the best technique to solve the problem.
Let’s discuss a US bank that used the Machine Learning technique to understand the discounts offered by their private bankers to the customers. It was claimed that they offered discounts to valuable customers, but the analytics detected unnecessary discounts. After correcting them, in few months, the bank saw an 8 percent revenue growth.
The data science team structure could be Centralized, decentralized, or hybrid. The centralized structure is ideal if the company operates with a Center of Excellence (COE) with different business units. If analytics is just embedded into each business, it is decentralized, and if the centralized and embedded units are combined, they form a hybrid.
While the data architecture is considered, the top companies follow the centralized pattern of data within the business units. In this model, you have a Chief Data Officer, which is a leadership role. Then comes the data analyst, who is responsible for the data collection and interpretation. Next is the business analyst, who transforms the expectations into the analysis. The data scientists solve complex business tasks with Machine Learning and Data mining. Apart from these key roles, there are Machine learning engineers, data journalists, data architects, data engineers, and data visualization engineers.
McKinsey cites, the highest priority for 60 percent of the banks was a perfect pool of analytics professionals. Banks can start with small data scientist teams, then scale up to the other positions eventually.
IBM has stated that amongst all the job demands for Data Science and Analytics roles, 59% are in the finance and insurance sector.
To be a data science professional in the banking sector, you require both technical and non-technical skills. Firstly, you require Programming skills to deal with data science and big data. In-depth knowledge about data analysis tools and data science certification is desirable.
Coding knowledge is also preferred. Additionally, you must know open-source frameworks to analyze huge data sets quickly. Data science professionals must be well versed in the analytics engine, streaming modules, Machine Learning, Artificial Intelligence, statistical and data visualization tools as well.
The non-technical skills include communication, teamwork, decision-making skills, and research skills.
From 2020-2021, about 60 percent of the surveyed companies employed more than 50 data science professionals in their firms. The banks excel in customer service, security, prediction and forecasting, customer sentiment analytics, profiling, segmentation, marketing, and many other segments with the data science strategy. Hence, data science plays a crucial role in providing the best experience to the banks and customers.