In the past few years, fintech companies have emerged and grown tremendously. These companies have disrupted the traditional financial industry by offering groundbreaking solutions that cater to the dynamic needs of consumers and businesses.
However, within this evolving landscape, effective risk management has appeared as a critical element for ensuring the success of fintech ventures. These companies must regulate risk exposure and keep it within acceptable limits to ensure long-term stability. It is possible with a credit risk assessment that enables companies to assess a borrower’s creditworthiness. And the technology that facilitates accurate credit risk assessment is Big Data Analytics.
Data analytics in finance is rapidly revolutionizing lending decisions. It enables fintech companies to create a more comprehensive and dynamic portrait of potential borrowers and minimize their risk profile.
In today’s blog, we will understand financial data analytics and how it can help lenders improve their risk assessment and make data-driven decisions.
The Traditional Credit Scoring Model
The credit scoring model is the bedrock of lending decisions. It involves estimating an individual’s creditworthiness based on their historical financial transactions. The statistical analysis considers factors like the individual’s payment history, length of credit history, amounts owed, etc. Based on this, financial institutions decide whether to extend or deny credit.
However, the traditional credit scoring model has its own set of limitations. One of the most prominent limitations is its static nature. Traditional credit scores rely on historical data, offering a picture of a borrower’s financial behavior up to that point. It can result in a narrow view of an individual’s creditworthiness, especially for those with limited credit histories.
Additionally, the traditional model fails to capture the complete spectrum of a borrower’s financial capabilities. For instance, capturing the possibility of income from unconventional financial instruments such as the gig economy is often only possible sometimes. As a result, many deserving borrowers are either deprived of credit or offered less feasible terms simply because their financial reality needs to align with the established criteria.
Such limitations make the traditional credit scoring model more and more outdated. There is a mounting need for a more adaptive approach that embraces the power of financial data analytics to provide a more holistic evaluation of an individual’s creditworthiness.
The Data Analytics Revolution
To combat the limitations of traditional credit scoring models, the financial industry has started using the power of big data and analytics to reinvent lending decisions.
Data analytics in finance allows financial organizations to integrate comprehensive and accurate data from diverse sources to evaluate a borrower’s creditworthiness better. It consolidates multiple data points, enabling lenders to make more informed decisions regarding loan approvals, credit limits, and interest rates.
Additionally, data analytics allows lenders to categorize their customer base and create personalized offerings based on individual borrower profiles. By evaluating each borrower’s financial patterns, behavior, and credit score, lenders can tailor loan products and repayment conditions to match the unique financial capabilities of each segment. It improves customer satisfaction and increases the likelihood of successful lending outcomes.
Incorporating Alternative Data
Alternative data includes many data sources extending beyond financial statements and reports. It contains references like an individual’s social media activity, online behavior, and transaction history. It often includes less familiar sources like utility bill records and rent payment histories.
Financial data analytics enables lenders to integrate such non-traditional sources of information to improve the accuracy of credit assessments. It ensures a more updated view of a borrower’s financial behavior and facilitates informed lending decisions. It also helps to create a more inclusive and fair lending environment as individuals are assessed based on factors that genuinely showcase their creditworthiness.
Predictive Modelling and Machine Learning
Financial data analytics allows lenders to perform accurate risk assessments by leveraging predictive models and machine learning algorithms. Lenders can evaluate past loan performance and assess key risk indicators to make precise predictions about future borrower behavior.
This proactive approach helps mitigate risk profiles and tailor repayment terms to individual borrower profiles. This results in reduced defaults and enhanced portfolio performance.
Example: DataVisor
DataVisor is an example of a fraud detection engine that leverages predictive models. It accurately assesses the probability of fraud across various financial transactions, from credit card purchases to loan applications.
Currently, a leading US Bank is deploying DataVisor. The incredible tool has helped increase the interception of fraud in online loan applications by 30%. It has led to over $4M in annual savings.
Real-Time Decision Making
Financial data analytics facilitate real-time decision-making by transforming how loans are assessed and approved. Continuous monitoring and analysis of a borrower’s economic behavior and the integration of external data sources provide current insights into their financial status.
The availability of up-to-date information streamlines the lending process and ensures real-time decision-making.
Example: LendingClub
LendingClub is a leading US-based peer-to-peer lending platform. It uses real-time data analytics to evaluate borrower’s creditworthiness and initiate quick loan approvals. It monitors the financial performance of each borrower in real time, which helps to manage loan portfolios effectively.
Mitigating Bias and Enhancing Fairness
Several biases can influence modern lending practices. These biases can stem from historical data or sometimes even unconscious human judgment. Financial data analytics can address these issues and promote fair lending practices.
With data analytics, financial companies can create equitable credit evaluation models. They can assess an individual’s credit score on multiple factors, leveling the playing field for borrowers with limited credit history, such as young adults.
Real-World Successful Use Cases
One real-world example of a fintech company using data analytics for lending processes is Upstart.
Upstart is a startup launched by ex-Googlers. The company leverages the power of AI and machine learning algorithms to offer loans to users with limited credit histories but a high earning potential.
The company gathers and analyzes a vast range of data points that extend beyond traditional credit scores. It includes factors such as education, field of study, income potential, and employment history to identify a borrower’s creditworthiness. Based on the accumulated data, the company creates personalized loan offers that match every individual’s unique financial profile.
Technology integration has helped Upstart make the lending scenario much more accessible and equitable. Besides, it has benefited the company in many ways, helping them secure:
- Increased approval rates
- Faster loan processing
- Lower default rates
Future Trends and Implications
Data analytics in finance is a rapidly evolving field constantly shaped by technological advancements and regulatory changes. Many future trends will surface in the coming years. The most prominent ones include integrating alternative data, personalized financial products, the rise of alternative lending options, and many more.
But at the same time, financial data analytics also raises specific challenges that financial organizations must address. These challenges include:
- Data privacy: As lenders collect and store sensitive borrower information, there is a high risk of data breaches and privacy violations.
- Model interpretability: Machine learning models may lack transparency, making explaining lending decisions to borrowers and regulators challenging.
- Data quality: Poor data quality can result in faulty insights and inaccurate decision-making.
- Bias and impartiality: Data analytics models can involuntarily spread biases in historical data, leading to biased decisions.
- Customer trust: Borrowers may be concerned about how the financial companies use their data, causing potential trust issues.
Financial organizations must address these challenges to fully utilize the benefits of financial data analytics while managing the associated risks.
Conclusion
Data analytics is genuinely revolutionizing the lending landscape. It can help lenders accurately assess the creditworthiness of each borrower, improve risk assessment, and make informed decisions. Comprehensive data integration, customer segmentation, and real-time data monitoring empower lenders to streamline the lending process, tailor their offerings, and proactively manage risks.
At Algoscale, we understand the power of financial data analytics and how it can transform our client’s business. With our bleeding-edge capabilities and expertise in the field, we can be your trusted partner in harnessing the full potential of data analytics for smarter lending decisions. Get in touch with us today to know more.
