The Coming Shockwave: How Central Bank Digital Currencies Could Reshape Credit Risk

Introduction

The financial world is on the brink of a seismic shift. Central Bank Digital Currencies (CBDCs) are no longer hypothetical—they are becoming a global reality. With China’s digital yuan in mass pilot use, the European Central Bank advancing its digital euro plans, and the U.S. Federal Reserve exploring its own framework, CBDCs are poised to rewire the mechanics of monetary exchange.

While most attention is focused on CBDCs' implications for payments, privacy, and monetary policy, a more subtle yet powerful transformation is looming: the reshaping of credit risk. As money evolves from paper to programmable, the architecture of credit—who gets it, how it's measured, and what risks emerge—may be radically altered. In this article, we explore how CBDCs could fracture the traditional liquidity landscape, disrupt credit supply chains, and force risk professionals to rethink the foundations of credit assessment.

The CBDC Landscape: What’s Coming and Why It Matters

CBDCs are digital representations of a country’s fiat currency, issued and backed by the central bank. Unlike cryptocurrencies, CBDCs are centralized and regulated. Their primary appeal lies in bolstering payment efficiency, financial inclusion, and monetary sovereignty. As of early 2025, over 130 countries—representing 98% of global GDP—are exploring or developing CBDCs, according to the Atlantic Council’s CBDC tracker.

China’s digital yuan (e-CNY) is arguably the most advanced, with over 260 million wallets issued and integration across transport, utilities, and retail payments. The European Central Bank is progressing with the digital euro’s legislative framework, while the U.S. Federal Reserve is testing prototype scenarios for a wholesale CBDC focused on interbank settlements.

Beyond efficiency, CBDCs offer programmable features—transactions can include embedded logic, such as expiry dates or usage restrictions. This opens up innovation but also regulatory risks. Their adoption could eventually reduce the need for intermediated banking, potentially draining deposits from commercial banks and altering the credit landscape.

Credit Risk 101: What We Know (and What Might Break)

Credit risk refers to the likelihood that a borrower will default on their financial obligations. Traditional models assess factors like borrower creditworthiness, income streams, collateral, and macroeconomic variables. These assessments rely heavily on data collected via banks and financial intermediaries who process transactions and manage deposits.

In today’s system, banks play a dual role: they extend credit and act as gatekeepers of liquidity. They aggregate funds from depositors and use them to lend to individuals and businesses. This function makes them critical nodes in the credit risk ecosystem.

However, the emergence of CBDCs may bypass these nodes entirely. As people and companies adopt CBDC wallets to store and transfer value directly through central bank infrastructure, traditional deposit volumes could drop. This disintermediation could limit banks’ lending capacity, distort traditional risk models, and reduce credit availability.

CBDCs and the Fragmentation of Liquidity

One of the less-discussed consequences of CBDC adoption is the fragmentation of liquidity. Programmable digital currencies could enable users to bypass banks altogether, transferring money peer-to-peer or through decentralized finance (DeFi) platforms. This would shrink the pool of deposits available to banks and potentially dry up the supply of lendable funds.

A report by the Bank for International Settlements warns that CBDCs may lead to “deposit flight” during times of crisis, as customers move funds from commercial banks to government-backed digital wallets with perceived lower risk. For smaller lenders, this liquidity squeeze could become an existential threat.

This dynamic could also impact vendor risk assessments. Suppliers reliant on invoice financing might see credit terms tighten or evaporate entirely. Financial health indicators, often based on access to short-term liquidity, would become more volatile and harder to interpret. Risk managers will need to adapt their scoring and early warning systems accordingly.

The Second-Order Risk: Disruption in Credit Supply Chains

CBDCs could affect not just banks but the entire credit supply chain. Businesses that depend on working capital loans, trade finance, or invoice factoring might face higher scrutiny or delayed disbursements due to uncertainty in banking liquidity. Small and medium-sized enterprises (SMEs), already underserved by traditional lenders, may be hit the hardest.

According to a 2024 World Bank study, over 65% of SMEs in emerging markets rely on local credit unions or small banks. If these institutions face deposit shrinkage due to CBDCs, their lending portfolios could shrink significantly. This could trigger a domino effect across sectors like manufacturing, logistics, and retail, where tight cash cycles are critical to survival.

Further complicating the issue is the growing automation in credit evaluation. AI-driven lending platforms may lack the contextual understanding to adapt quickly to CBDC-induced liquidity shifts. This might result in increased false negatives (creditworthy businesses denied loans) or false positives (risky ventures mistakenly approved). As highlighted in the AI-powered risk strategy article, these biases can significantly affect credit integrity.

Stress Testing for the CBDC Era

Given the profound uncertainties CBDCs introduce, organizations must evolve their stress-testing frameworks. The goal is no longer to just test for recession or inflation shocks, but also liquidity reallocation, credit contraction, and cross-border CBDC volatility.

One key tactic is to create simulation models where CBDC adoption triggers a tiered banking model—where only large institutions retain sufficient deposit bases while smaller lenders collapse. Enterprise risk managers can model the exposure of their customers, suppliers, and counterparties to these scenarios.

Additionally, incorporating CBDC-specific metrics—such as wallet adoption rates, programmable transaction rules, and interbank settlement volatility—into simulations will allow firms to remain resilient. Our guide to stress testing risk culture provides a detailed framework adaptable to CBDC-related variables.

AI, CBDCs, and the Rise of Credit Surveillance

CBDCs promise transparency, but this transparency may come at the cost of increased surveillance. Every transaction in a CBDC ecosystem could be logged at a granular level. While this aids fraud detection and regulatory oversight, it also opens the door to hyper-automated credit profiling.

AI systems trained on real-time CBDC transaction flows may become the new credit bureaus. These systems could evaluate behavioural patterns—spending habits, income consistency, transaction timing—and assign scores accordingly. While powerful, this approach raises ethical concerns around bias, overfitting, and systemic exclusion.

If not regulated carefully, AI-driven scoring models could replicate the same discriminatory biases embedded in earlier systems. This risk is detailed in our article on AI enterprise risk monitoring, where transparency does not always mean fairness. Risk managers must evaluate AI explainability and fairness metrics alongside accuracy to avoid regulatory backlash and reputational harm.

Conclusion: From Shockwave to Strategy

CBDCs represent both a threat and an opportunity for enterprise risk leaders. While they offer efficiency and control, they also upend the foundations of liquidity and credit modelling. The domino effect could be felt across lending, vendor risk, trade finance, and beyond.

To navigate this shift, risk managers must expand their lens. They need to understand CBDC mechanics, monitor pilot programs, adapt stress tests, and evaluate second-order impacts. Most of all, they must bridge the gap between innovation and stability—ensuring that as money evolves, so too does the architecture of risk.