As the lending industry becomes more competitive, transparency and accountability in decision-making are critical for a productive and long-term relationship between stakeholders.
Customers want to make informed decisions and feel empowered when making a choice. In this scenario, transaction data analysis can strengthen the relationship between lenders and borrowers. It gives lenders critical insights and helps them build a more transparent relationship with their customers.
Each customer interaction generates data, which provides valuable insight into customer behaviour and financial habits. This helps financial service providers make decisions based on objective data analysis, fostering trust and being more equitable.
In this blog post, we explore the transformative power of transaction data analysis in contemporary lending processes.
What Is Transaction Data Analysis?
Data analytics is helping businesses across industries improve their functioning and marketing strategies. Each transaction, whether between a debtor and creditor or a buyer and seller, offers valuable insights into user behaviour.
Lenders face credit and operational risk in their day-to-day functioning; using transaction data analysis can help them minimise this risk. Financial and bank statements, GST returns, or digital banking records are some sources lenders can examine to gain meaningful insights. They get a comprehensive view of the account holder’s financial position, credit health, spending habits and budgeting patterns.
Analysing bank statements and other financial statements helps lenders offer personalised solutions to their customers and make a more comprehensive risk assessment for them. Identifying unusual spending patterns or transactions can help identify fraudulent activity.
This way, lenders can leverage transaction data analysis to deliver more value to their customers and mitigate risks.
How Does Transaction Data Analysis Improve Trust and Accountability?
Here’s how transactional data analysis provides lenders with a more comprehensive and objective view and promotes trust and accountability in the following ways:
Analytical Approach to Credit Assessment
Lenders assess the applicant’s creditworthiness before they sanction a loan. An accurate credit assessment is essential for the lender to minimise their default rate and credit risks. A credit health check benefits the borrower, as well, and ensures they do not take on more debt than they can repay or fall into a debt trap.
Transaction data analysis promotes a transparent and more holistic approach to credit assessment. Lenders can trust the study to help them make objective decisions. Borrowers gain visibility into factors that impact the lending decision, empowering them about their financial choices.
They can be sure that the decision to sanction or deny a loan depends on data and facts and not some subjective analysis. The benefits extend beyond the lender-borrower relationship as the industry, as a whole, can cultivate a higher level of trust and accountability.
Transparent Approach to Loan Pricing and Terms
Transactional data analysis gives lenders a more comprehensive view of the customer profile. They can predict the borrower’s likelihood of default with more precision and set loan terms and rates accordingly.
Sometimes, the lender may offer the same loan type at different rates to different applicants, which could lead to confusion and mistrust among applicants. The difference in rates and loan terms (collateral requirement, Loan-to-Value Ratio) may be due to the disparity between the risk profiles of the applicants.
For example, the lender may charge an applicant with a higher default risk probability more interest or ask them for a larger downpayment. An objective approach to risk assessment helps lenders present their decisions more credibly to their clients. Borrowers know their profile determines the loan terms, prompting them to be more responsible financially.
Data-driven risk assessment paves the way for a fairer and more transparent ecosystem. Lenders can have interest rates, terms, and conditions that reflect the level of risk they are willing to take without being unfair to the borrowers.
Promotes Financial Inclusion
Often, large segments of the community do not have access to formal credit channels as they do not have any credit history, collateral or a guarantor. Lenders generally rely on traditional credit scoring models to assess the creditworthiness of an individual and sanction or deny a loan based on this assessment.
Applicants may feel frustrated and confused when lenders deny them a loan. They may not understand the reason for loan rejection or what they can do to access credit in future. Lenders can help individuals and small businesses overlooked by conventional lending models become a part of the formal lending channel by using unorthodox credit assessment tools for credit appraisal.
Transaction data analysis goes beyond the conventional credit assessment methods and promotes financial inclusion. Lenders make objective credit assessments to ensure highly leveraged applicants or those with insufficient inflows to service debt are denied loans but, the deserving are not left out.
Lenders can build trust using more transparent decision-making tools and position themselves as socially and ethically conscious players. So, it is a win-win for all stakeholders; deserving applicants access credit, lenders can increase their customer base without additional risk, and the industry becomes more inclusive and robust.
To Sum It Up
Transaction data analysis supports lenders in making data-driven and objective decisions that help them improve their efficiency and profits. This innovative tool benefits customers and fosters trust between stakeholders; it contributes to establishing a more robust and accountable financial framework.
Lenders can strengthen their operations and reap long-lasting benefits by investing in innovative solutions. Finezza offers a comprehensive analytics suite that leverages Machine learning and Big Data Analytics to analyse transactions and offer lenders actionable insights.
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