Good data can mean the difference between profit and failure in financing. Making wiser choices about who to lend to and how much at what rate depends on knowing your borrower and the environment.
Across all industries, data is undoubtedly the most important resource. By 2024, many company executives will be focused on ensuring that the data they now have is in a healthy state for AI tools and other decision-making. However, this is just the beginning of data governance.
Encouraging individuals to quit number-crunching and start utilizing the AI and ML tools at their disposal while putting the proper procedures in place is essential for collecting high-quality, clean data. But in order to get the most of these technologies, you must first make sure that the data is of high quality and then learn how to analyze it properly. These three data questions will help you develop a data strategy that will help your company thrive in the AI era as you get ready for 2024.
Quality Of Data
When using AI or ML tools in your organization, data quality is a non-negotiable need. But, if your underlying data is correct and comprehensive, these technologies will only produce useful, reliable results. They could just as easily be inventing solutions in the absence of a firm foundation. No one is benefited by that.
AI and ML tools are barely limited to your own data. Data on the economy, customers, competitors, and the sector should also be taken into account. Assuring ownership and control in lending requires additional consideration of federated data and actuarial principles. In order to make a significant contribution to your results, each of these outside sources needs to fulfill data quality requirements.
The days of massive Excel file data dumps are long gone. Spreadsheets still provide a lot of space for misunderstanding, but they have developed into data warehouses that standardize the data and make it useable for analytics and reporting. In order to establish and implement regulations that promote high-quality data collection and cleaning, a data governance committee is essential.
Quantity Of Data
Any company considering the use of AI or ML now or in the future should prioritize data collection. Many companies want to take that exact action. To ensure that your lack of access to data does not limit your potential, make sure that your company charter and customer agreements clearly explain your data gathering policies.
An AI or ML tool can examine more variables if it receives as much data as feasible. The more data that is provided, the more accurate the findings will be. This is not because every decision will take into account every data point, but rather because a more robust dataset will enable you to go deeper and find the specific data points that are important.
Quality Of Partner
It’s likely that you are not handling this data inflow by yourself. To benefit from these extraordinarily powerful tools, you don’t have to be a specialist in AI or ML because collaborations may be established to guarantee success. This could be in form of internal knowledge or an outside partner.
The choice and terms of a possible loan are determined by combining multiple types of important data. Contributing elements include risk considerations, industry data, and borrower data. A quality partner will not base their loan choice solely on a borrower’s creditworthiness, which is a common error in loan decisioning. Instead, they will use these three datasets to create linkages that forecast the likelihood and severity of default.
A lender may unintentionally give a loan with a high payment-to-income ratio if they just use conventional creditworthiness metrics. This kind of high-risk loan puts both the lender and the borrower at danger of failure. By creating links within already-existing datasets to more accurately assess whether a loan will be profitable, a good partner will protect you from that disappointment. Lenders can select a term or loan structure that better suits their portfolio and their clients by examining alternative features and possible loss, such as the number of payments made or credit inquiries during the previous year.
Formulating The Comprehensive Data Plan
In the era of artificial intelligence and machine learning, a comprehensive data strategy will link a large amount of high-quality data with a high-quality data partner to deliver crucial business insights. Businesses that don’t have a strong data foundation run the danger of slipping behind as these tools become increasingly common and valuable. It’s time to consider these three data Qs and consider how they could improve your position ahead of AI development.
