The banking, financial services, and insurance (BFSI) firms have embraced net banking and automated financial analytics. Besides, investment strategists want risk insights to monitor stock markets and calculate fair prices using analytics. This post will explore the role of data and financial analytics in the BFSI sector.
What is Data Analytics in BFSI?
Data analytics involves conducting computer-aided trend identification across extensive databases. BFSI firms implement data analytics solutions to evaluate financial risks and create high-quality investment research reports.
For example, banks and non-banking financial companies (NBFCs) want reliable creditworthiness assessments to determine whether a person can repay the borrowed capital. Therefore, credit bureaus develop advanced analytics solutions to generate credit scores. Individuals with a healthy credit score often settle their debt. So, banks give more generous loan terms to such customers.
Likewise, the financial data and analytics in the BFSI sector also enable fraud prevention systems to fulfill their role more efficiently.
How Data Analytics Plays an Important Role in the BFSI Sector?
Investment banks, insurance companies, government departments, and financially responsible individuals desire healthy returns on investment (ROI). So, they require professionally validated investment research reports to optimize portfolios and redistribute financial risks.
However, manual research efforts for cash flow analysis or project feasibility studies are time-consuming. Only prominent organizations with an international footprint can afford to build and train in-house teams for these processes.
Besides, if you take too long to determine investment strategies, you may lose some attractive opportunities due to delayed reporting. Therefore, novel insight extraction tools are essential when building real-time investment intelligence.
Consider the following strategic benefits of financial data analytics solutions to learn why their role is important in resolving all these issues in the BFSI sector.
Benefit #1| Multivariate Risk Modelling
Numerous factors affect stock markets and prevailing interest rates. Sometimes, you witness unfavorable investor sentiment. Otherwise, the economy might grow at a slow pace. A high unemployment rate decreases consumer spending, weakening corporate revenue streams.
Think about how these elements hurt companies and stakeholders like employees or customers in the BFSI industry. Therefore, financial institutions implement multivariate risk analytics to track market trends and devise appropriate response strategies.
Simple statistical methods include bivariate modeling. However, financial analysts must repeat statistical inquiries for each pair of two variables. Multivariate statistical modeling in advanced analytics solutions eliminates this inefficient process, saving time and resources.
Benefit #2| Data Integrity Assurance
Poor data quality gives you misleading financial insights. Later, your clients will get harmful investment recommendations due to this mismanagement of data quality. Therefore, financial service providers must devise techniques to rectify data quality issues.
Professional equity research firms and macroeconomic data analytics solutions prioritize the following considerations in dataset integrity assurance.
- Completeness implies that your databases are free of empty values. Data gaps cause problems in modeling and calculations, but you can remove them using automation.
- Freshness means your investment research reports and other datasets must be relevant to new competitive challenges. After all, outdated or unrelated data introduces inconsistencies in BFSI sectors’ trend analytics.
- Accuracy is vital, but you must regularly validate the data to confirm it. Remember that online and offline resources can supply inaccurate industry foresight due to human errors or outdated calculation models.
- Efficient storage finds and merges duplicate data entries to address redundancy issues in datasets. After all, storing identical data values on multiple locations outside the standard backups implies that your data management system is wasting your IT resources.
Benefit #3| Energy-Efficient Data Processing
International research firms serve more than a million consumers. You can also discover similar conditions after observing popular banks and insurance companies. If institutions process data related to all these stakeholders, they strain their IT resources and consume significant electric power.
Uncoordinated analysis activities burden BFSI corporations’ computing capabilities. Eventually, high energy consumption results in an increased cost of operations. Therefore, sustaining large-scale insight extraction activities becomes more expensive.
Most companies must continue offering financial services to address the needs of a growing consumer base. They can use efficient data analytics solutions to lead the BFSI sector in reducing energy consumption.
Benefit #4| Confidentiality of Intelligence
Data analytics firms apply end-to-end encryption (E2EE) and prevent unauthorized modification of databases or extracted insights. Therefore, you are increasing your corporate governance compliance by focusing on data protection.
Insider trading, corporate espionage, and cyber-attack also threaten net banking solutions. Simultaneously, the BFSI sector must serve the role of e-commerce enabler and leverage data analytics solutions to understand customer behavior.
Two benefits of financial analytics tools are improved digital governance and assured data confidentiality. Customers, regulators, and investors often appreciate companies that secure their users’ data utilizing the latest cybercrime prevention tactics.
Use Cases Demonstrating Data Analytics in the BFSI Sector
Every BFSI company customizes its offerings to help a specific category of clients. Therefore, the examples of BFSI data analytics vary across financial service providers. Remember how insurance companies differ from equity research firms or commercial banks. Their ideal client personas and revenue models are remarkably distinct.
This dynamic also suggests tailoring financial technologies to help respective companies concentrate on their primary products and services. Furthermore, risk assessment for a health insurance scheme includes variables that are distinct from risk analytics for vehicle insurance.
Modern software platforms and consulting firms optimize their services to meet client expectations. So, it is inevitable that you will notice the following sector-specific use cases of data analytics in BFSI.
1| Creating Investment Research Reports
Fair price calculations allow institutional and retail investors to avoid wasting resources on overpriced stocks. Meanwhile, company performance datasets provide more reliable insights into its long-term growth potential.
An investment research report collects and presents the data on the following considerations to generate buy-hold-sell advice.
- Company assets and liabilities,
- Risk exposure,
- Fair price estimation,
- Price movement forecasts,
- The industry growth outlook,
- Financial fundamentals,
- Corporate leadership,
- Sustainable business practices.
Investment research firms utilize data analytics solutions to enhance their recommendation reports.
2| Preventing Fraud, Tax Evasion, and Money Laundering
Data analytics in BFSI helps financial institutions identify misuse of their services. You can use analytics to audit customer accounts and investigate the authenticity of transactions. Therefore, banks and accounting firms can quickly identify fraud attempts.
Additionally, government departments track transactions to curb tax avoidance and money laundering. Analytics also allows them to build intelligence on how individuals and corporations conduct financial transactions.
This use case emphasizes creating a financially responsible mindset among the stakeholders in the BFSI sector. Also, one of its components includes using analytics to simplify and democratize tax estimations to encourage citizens to comply with tax laws.
3| Reducing Loan Non-Repayment Risk
Credit bureaus tell the bank whether a person or a corporate borrower can settle the loan according to the agreed-upon repayment terms. If financial service providers do not verify creditworthiness, they risk attracting regulatory restrictions aimed to reduce “bad loans.”
NPA means non-performing assets, and bad loans increase a bank’s NPAs. Therefore, the bank can offer more generous loans only if it brings NPAs under control. Helping lenders minimize such risks originating from borrowers’ behavior is part of the role of data analytics solutions optimized for the BFSI sector.
Moreover, social listening and spending analysis provide financial institutions with alternative data. The subsequent insights in data analytics can assist lenders in rationalizing credit scores or ratings.
4| Monitoring Employee Productivity
Bank managers, investment advisors, and insurance agents interact with many people daily. You want them to secure new clients and offer support to existing customers. However, some operations might rely on legacy technologies and cause operational inefficiencies.
Therefore, the BFSI sector utilizes data analytics solutions to minimize inefficient activities associated with a job role. Besides, sorting business intelligence datasets through obsolete software platforms makes you vulnerable to IT risks.
Likewise, certain operational inefficiencies originate from employee behavior and work ethic. For example, consider the case of unsupervised screen time spent on social media platforms during work hours.
Data analytics helps you identify such behaviors. Later, you would notify the concerned staff member to practice well-calculated time allocation for professional and personal obligations.
5| Building Regulatory Intelligence
The budget speech is not the only component that must attract the attention of financial service providers. Any professional experienced in the BFSI sector can attest to the role of data analytics solutions in compiling more exciting regulatory intelligence.
BFSI companies must watch out for international treaties and import-export tariffs. Currency fluctuations also impact billable amounts. Legal mandates in North America, Europe, and Asia might impact investor sentiments in South America, Africa, and Australia.
Financial professionals cannot gather intelligence through manual data collection techniques in this competitive landscape of hyper-globalization. For example, social media influencers have begun changing consumer attitudes toward products and services offered by some brands.
The international policy dynamics and related regulations like privacy laws affect the financial systems. So, building suitable intelligence datasets has become critical. Otherwise, a foreign government may charge your client for non-compliance with regional laws.
Conclusion
Companies in the BFSI sector appreciate how modern technologies can streamline their operations. Data analytics solutions are some of these beneficial tools that help financial service providers conduct comprehensive investment research.
Financial analysts also help governments and insurance companies analyze their risk exposure. Besides, many enterprises, banks, and accounting firms intend to customize data analytics to improve corporate governance.
At the same time, authorities and cybercrime experts explore the application of analytics in preventing fraud and money laundering. Nevertheless, data integrity remains central to insight extraction and effective strategy development.
A leader in data analytics solutions, SG Analytics, assists the BFSI sector in developing accurate financial models to evaluate investment decisions. Contact us today to acquire high-quality analytical support in macroeconomic research and financial intelligence.
