What makes the RBI concerned about algo model-based lending

A lack of transparency raises valid concerns about accountability and fairness in lending practices.

Resonating with a cautionary tone, RBI governor Shaktikanta Das recently articulated concerns over algorithmic model-based lending. This reiterates the regulatory commitment to maintaining the stability and resilience of the financial sector in the face of evolving fintech dynamics. Algorithmic models, powered by artificial intelligence and machine learning, often operate as ‘black boxes’, making it challenging to decipher the rationale behind their decisions. This lack of transparency raises valid concerns about accountability and fairness in lending practices. Many lending models are built using algorithms. The fear is that these algorithms, if not rigorously monitored, and if not traceable to root cause, could inadvertently perpetuate biases or discriminate against certain variables and also lead to bad credit decisions.

In response to potential risks, the banking regulator recently raised the risk weights for consumer credit exposure by 25 percentage points to 125 percent. This reflects concerns about the possibility of undue risk accumulation in the system arising from information gaps in these models, potentially resulting in a dilution of underwriting standards. The RBI has been kind and patient in asking the boards of its regulated entities to be vigilant, and to take accountability about lending models and lending exposure across segments. To be fair to the RBI, these are essential roles of the individual boards to ensure prudential and risk-managed behaviour in their enterprises.

Updating Algorithms Dynamically

To be fair to those financial institutions that are risk-prudent, they revamp their credit models every 12-18 months and update their algorithms dynamically. Regulators should make this a rule, instead of worrying about the use of algorithmic models. In reality, algorithm-based lending has not only reduced operational costs but has also expanded the outreach of financial services providers. The collaboration between banks, NBFCs and fintechs has increased, leading to the introduction of innovative products, services and business models.

However, the apprehensions surrounding algorithmic lending must be contextualised within the broader landscape of financial innovation. The increasing reliance on newer datasets including unstructured data, and digital technologies is an inexorable shift, driven by the need for efficiency, speed and adaptability in financial services. Algorithms, when designed and monitored judiciously, have the potential to enhance credit assessment, reduce processing times and democratise access to financial products.

Mainstreaming Fintechs

Traditional regulatory frameworks, designed for a bygone era of manual processes, limited datasets and face-to-face interactions, have to evolve. As much as financial regulators try to keep fintechs away as their regulated entities, it is a matter of time before they will be mainstreamed. The financial regulators have to catch up with digital competencies before they can open the gates for fintechs into their universe. So, the deeper question: is regulatory sandbox, a polite term for ‘please wait while we upgrade’?

Finance, in the digital age, will have both the threads of algorithmic usage and traditional financial principles. Asset liability management, risk management, pricing of risks and governance will remain as indispensable in the digital era as they were in the analogue world. The challenge lies in recalibrating these principles to harmonise with the speed and scale of digital financial transactions.

As financial institutions increasingly leverage data analytics, artificial intelligence and machine learning, the focus must shift from harbouring reservations to fostering a comprehensive understanding of these technologies. Regulators, therefore, bear the onus of enhancing their understanding, competencies and capabilities around everything that connects digital and finance. This involves not only scrutinising the algorithms themselves but also comprehending the underlying technologies and their implications for systemic stability.

Appreciating the regulatory concerns surrounding transparency in the lending process is essential for fostering better and preventive risk management. Transparency acts as a cornerstone in maintaining the integrity of financial systems, ensuring that lending decisions are not only fair but also comprehensible. A transparent lending process facilitates more effective risk assessment, enabling regulators to identify and address potential vulnerabilities proactively. Moreover, transparency is instrumental in safeguarding the long-term interests of consumers, as it builds trust in financial institutions and their practices.

On the regulatory front, concerns emerge in tandem with the market and investor exuberance characterising fintechs, banks, and non-banking financial companies (NBFCs). The surge in enthusiasm is largely fuelled by the easy availability of capital, as investors avidly pursue opportunities in the digital-based finance space. Regulated entities, eager to capitalise on this influx of capital, might be tempted to prioritise rapid expansion over rigorous risk management. Unchecked optimism could potentially lead to systemic vulnerabilities. This exuberance poses challenges for regulators, as the frenetic pace of credit growth may outstrip regulatory ability to assess and mitigate associated risks adequately.

The new age finance is also about financialisation of data and digital, as much as it is perceived to be digitalisation of financial transactions. Striking a balance between commercialisation of innovation and agile-regulation is the fulcrum upon which the future of finance is. The need for supervisory capabilities tailored to the digital age cannot be overstated, as they transition from active participants to proactive stakeholder in the convergence of digital and finance.

Srinath Sridharan is author, policy researcher and corporate advisor. Twitter: @ssmumbai. Views are personal, and do not represent the stand of this publication.