Data science is one of the rapidly growing fields. And it can transform the finance industry. But, solving financial problems innovatively is more important. Indeed, many issues affect the BFSI companies.

Now, you will learn about the challenges and opportunities of data science in finance.

What is Data Science in Finance?

Data science is the application of data mining, machine learning, and statistical analysis. And this technique is great for discovering patterns in complex data sets. Also, data science services are multi-disciplinary. For BFSI entities, its databases and visualization solutions are highly valuable.

So, what are the challenges and opportunities for financial data science? We must know this to get a realistic perspective of using data science in the BFSI industry.

Challenges Faced by Data Science in Finance and BFSI Industry

As you know, data science is a relatively new concept. And implementing any change in an established organization takes time. This section lists the challenges that data scientists face in financial systems.

Data availability. Data is not always available for traditional/paper transactions. Data may be missing or stored in a format that is not useful for analysis. It is also likely that it would be stored in multiple locations. E.g., international banking, peer-to-peer lending platforms, etc.

Reliability of data. Transaction data may be unreliable because it has not been validated for accuracy. It might need audits to test its completeness. Otherwise, the use of less reliable data results in incorrect conclusions about the risk assessment of the bank.

Structured vs. unstructured data. Today, banks and insurance companies use different data types. It is unstructured in the form of textual information in emails. And it can have a structure like spreadsheets. That would be called semi-structured data. These are hard to analyze with traditional statistical methods. But newer data analytics services have made some progress here.

Cleanliness. In addition to being relevant and accurate, your data must be clean. You must guarantee data integrity for correct predictions. Remember that data quality affects the validity of your data science conclusions.

Opportunities for Data Science in Finance Industry

As the BFSI sector evolves, data science is becoming a key factor in this growth. We will learn about the different opportunities for data science services in finance.

The customer experience (CX). Data science can help improve the customer experience by understanding the customer’s needs. A bank can detect the satisfaction levels of the customers.

Personalized marketing. BFSI companies can learn more about their customer’s financial needs. And data science can help banks and insurance companies personalize their offers.

Automation of repetitive tasks. Data science services can be used to automate tasks that are monotonous and tedious, letting bank employees focus on more strategic roles.

Fraud detection. Another area where data scientists can make an impact is fraud detection. You can use it to detect fraudulent transactions or accounts. Banks need a system to find data patterns that indicate suspicious activity. Data scientists develop algorithms that identify such patterns quickly and accurately.

Borrower profiling. Due to social media, BFSI companies can learn more about their borrowers and lenders. Using data analytics services, these institutions can expand their KYC or know your customer records.

Efficient, secure transactions. Data science in finance is desirable for more efficient transactions. It can warn you against cybersecurity issues. Also, a bank can optimize its system to reduce transaction failure rates.

Risk management. BFSI players can predict the risk of losses on investments. Data scientists can help them in credit score analysis and social media sentiment analysis.

Customer segmentation. Text analytics helps companies identify groups within their customers. It is based on their behavior, purchasing power, or preferences. E.g., high net worth individuals. You can target them with tailored offers to increase conversion rates significantly!

Conclusion 

Data scientists are working day and night for better statistical modeling. And the banking sector can benefit from this. As of now, many insurance companies have adopted data science services. Also, risk assessment and valuation use data nowadays.

A modern BFSI company cannot stay competitive without reliable data science solutions. A good data scientist can understand the needs of banks and insurers and help them increase their borrowers and customers.

SG Analytics is a leading data science firm with an international presence. Contact us to learn how your company can use data science for financial interests.

Data Science in Finance – Challenges

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