09.01.2026 13:20
Despite having a high credit score, the number of people whose loan or credit card applications are rejected is increasing. With the behavioral risk analysis implemented by banks, not only the credit score but also the recent account activities, cash flow patterns, and risky spending habits of customers play a decisive role in credit decisions.
Despite having a high credit score, there is an increase in the number of people whose applications for loans or credit cards are rejected. With the new risk assessment approach implemented in the banking sector, not only the score but also the recent account activities and cash flow of customers have become decisive in the credit processes.
MANY CONSUMERS RECEIVE NEGATIVE RESPONSES
Recently, many consumers are receiving negative responses from banks despite having a good credit score. Banks have started to go beyond traditional scoring and examine customers' account usage habits in more detail. Regular income flow, spending patterns, and the consistency of financial behaviors are emerging as critical criteria for credit approval.
In the new approach, the method called "behavioral risk analysis" scrutinizes the nature of the money entering and leaving accounts. Regular salary deposits, bill payments, and standard expenses are considered normal, while frequent and unexplained transfers, sudden inflows and outflows of money, and unusual activities are evaluated as risk signals.
VIRTUAL BETTING PAYMENTS AS A RISK INDICATOR
Regular payments made to virtual betting and gaming sites are seen as a strong risk indicator from the banks' perspective. It is stated that such expenditures can lead to the rejection of loan and credit card applications on the grounds that they may weaken the customer's repayment capacity.
MONEY LAUNDERING DETECTED IN A SHORT TIME
Banks conduct these audits largely through automated systems. AML software used in the fight against money laundering and financial crimes, along with AI-supported analysis systems, can quickly detect unusual money movements. Customers identified with suspicious transactions are marked as "high risk" in the system, which can directly affect credit processes.
In credit decisions, in addition to the data from the Central Bank Risk Center and the Credit Registry Bureau (KKB), banks' internal analyses are also taken into account. AI-supported models influence the final outcome of loan and credit card applications by scoring criteria such as "inconsistent spending with income," "irregular cash flow," and "contact with risky sectors."