Credit Score: Why should Every Customer Transaction Count?

Importance of Analyzing Each Customer Transaction

Traditional credit scoring models often consider financial events like payment history, length of credit history and credit mix. This type of credit scoring model has limitations. It does not consider other transacting behavior of a customer. In modern credit scoring models, every customer transaction should be treated as a critical data point that can be used to reflect the customer's financial behavior and capability. By doing that lenders are not going to overlook smaller everyday customer transactions. These smaller transactions collectively provide a comprehensive view of a customer’s transacting habits and cash inflow trend. When lenders include all transactions, they accurately assess a customer’s risk profile leading to a more realistic credit evaluation. This holistic approach is particularly important in today’s digital world where the number of digital transactions is increasing. If financial institutions incorporate customer transaction data, such as? bill payments, deposits, withdrawals, and transfers into their credit score engine, then , they will have the opportunity to extend credit facilities to a broader range of customers, including those who may not have traditional credit histories. This, in turn, improves financial inclusion by making credit more accessible to underserved populations, helping them to build credit history and access other financial products.

Challenges in Analyzing Every Customer Transaction

Analyzing each customer transaction is neither straightforward nor easy. One significant challenge is the volume and diversity of transactions, which can range from small,? everyday purchases to irregular, large expenses. Processing and analyzing this data requires the right people and technology to identify meaningful patterns and trends, while ensuring data quality is maintained. Ensuring data quality is crucial because inaccurate or incomplete data can lead to faulty insights and misguided decisions. High-quality data allows for more precise analysis, enabling lenders to build reliable credit scoring models. When lenders gain right insights into their customers’ financial behavior, it helps to uncover hidden spending habits, preferences, and potential risks that are essential as inputs into the credit scoring model. Another challenge is the privacy and security concerns associated with collecting, storing, and processing detailed customer transaction data. Lenders must consider customers' rights regarding their data privacy.? With more countries implementing Personal Data Protection Acts (PDPA), lenders must ensure customer consent is obtained before incorporating their data into credit scoring models. As of this writing, more than 70% of countries worldwide have a PDPA in place. Based on this, lenders have no choice but to obtain customer consent before incorporating their data into the credit scoring model.

Addressing the Challenges and Implementing Solutions

Board and management engagement is crucial for the successful integration of every customer transaction into the credit scoring process. Their support ensures that adequate resources are allocated for developing the necessary infrastructure, such as data collection, storage, and Artificial Intelligence (AI) systems. With board and management endorsement, there is clear strategic alignment, allowing cross-departmental collaboration to enhance data accuracy and security. This support also facilitates stakeholder trust and transparency, ensuring that the use of transaction data for credit scoring is ethical, secure, and beneficial for both the institution and its customers. Another area that requires lenders’ to pay attention is obtaining of customer’s consent. By securing consent, lenders ensure that they are adhering to data privacy regulations, such as the Personal Data Protection Act (PDPA), which mandates that personal data cannot be used without explicit permission. This consent enables lenders to access and analyze a customer's financial history and other relevant data points to assess creditworthiness more accurately. It also fosters trust between lenders and customers, as the process is transparent and customers are aware of how their information will be used. By embracing modern technological solutions such as AI and considering data privacy, lenders have the opportunity to create credit scoring systems that are more inclusive, accurate, and reflective of a customer's true financial behavior.

About Emmanuel Damas

Emmanuel has twelve (12) years of experience in the Information and Communication Technology (ICT) domain. His technology experience cuts across several sectors including financial, education, manufacturing, telecommunications, health and transport. He has been involved in strategic and governance activities in relation to Information and Communication Technology (ICT) such as ICT policies and procedures design, Data analytics projects, Data migration projects, ICT system projects implementation, ICT Audits and Awareness trainings especially on data analytics and cyber security domains. Emmanuel's mission is to continue helping people and institutions in reaching their vision through adoption of effective ICT governance practices.