Reducing Regulatory Lag Using RegTech Derived from Current Practices in Financial Services Regulation

By | January 20, 2021

Scholars and lawmakers have long understood that regulation suffers chronic delay between the first appearance of a “regulable activity” and a government agency’s ultimate use of effective countermeasures. Regulatory lag (RegLag), which often results from Legal Lag. Legal Lag is a law and technology term that refers to situations in which “existing legal provisions are inadequate to deal with a social, cultural or commercial context created by rapid advances in information and communication technology”. Legal Lag describes how administrative agencies are slow to respond with needed regulation in real time.

Our article, “Predictive Regulation,” addresses this problem, and suggests predictive lawmaking as an approach that promises reduced regulatory social costs, while increasing the effectiveness of regulatory programs. We discuss the impact of Regulatory Technology (RegTech) on financial services, applying predictive lawmaking universally to other industry regulation. Our predictive regulation, derived from the predictive lawmaking approach, is anticipatory. This permits regulation to control the most harmful aspects of novel technologies and transactions while ultimately reducing the over-reaction of tardy and more restrictive regulations. These outcomes yield simultaneous benefits to (i) improving regulatory agency success, while also (ii) reducing compliance risks and the costs immediately and eventually borne by regulated entities.

RegTech Targets RegLag

Our predictive regulation approach utilizes RegTech tools, which are largely driven by ubiquitous innovation in information technology. It also applies “adaptive law” and “resilience-and-law” theory, which use adaptive management that facilitates social and eclogical resilience through moderate evolution in rules, standards, processes, and structures as the system adapts to changing conditions.  Adaptive management is iterative, relying on a series of experiments and practices that heed new information derived from feedback loops. It requires monitoring, learning, and deliberate changes from the constant update of predictions – all extremely useful when implemeting predictive approaches.

Our article envisions an adaptation of RegTech’s primary focus on compliance side information technology mechanization in financial services, applying such techniques more broadly into regulatory programs in various industries. RegTech methods can be applied across almost any regulatory program when deployed at different levels of governance: international, federal, state, local, regional, and private regulation by self-regulatory organizations (SROs)[1] and standards development organizations (SDOs).[2]

RegTech has a lot to offer, but its promise remains uncertain. Regulators endure well-established constraints in funding, technological lag, and recurring and fierce opposition from self-interested regulated entities as reinforced by libertarian philosophies.

Predictive regulation addresses some of the challenges by proactively identifying regulable activities and suggesting various approaches—a strategy that is in line with the major aspects of most agencies’ statutory mission.

Predictive regulation has great potential. Some administrative agencies already practice some adaptive management techniques in environmental matters (e.g., managing forests, wetlands, and river systems). We argue, however, that ALL regulators can and should learn to utilize RegTech tools to address trends and changing circumstances as currently seen, for example, in smart cities planning and management. In that context, it has already been widely shown how regulators can adopt, ideally with certain impotant limiations, surveillance technology in order to more effectively use data collection for the allocation and management of resources and predict future challenges and trends.

While rule promulgation typically lags the suspicion of future regulable activity or its first discernable appearance, RegTech can shorten RegLag delays. Predictive regulation enables the development of RegTech solutions.


The RegTech implementation we propose will most certainly disrupt previously stable legacy regulatory methods and compliance approaches currently taken by regulatory agencies and regulated entities. Disturbance is likely in most of the primary methods regulatory agencies currently use: (i) statutory mission interpretation, (ii) agency workforce training and adaptation, (iii) policymaking and rulemaking, (iv) monitoring and maintenance of regulatory systems, (v) investigations, (vi) sensor operations, (vii) data collection, (viii) enforcement, (ix) litigation and dispute resolution, (x) development and announcement of regulatory guidance, (xi) oversight of SROs, and (xii) agency hiring and procurement. However, the long term benefits of a RegTech-based predictive approach outweigh the inconveniences of adapting existing methods to the new model.

Our article, which zooms in on the financial industry, finds that successful RegLag solutions can help narrow RegLag delays. As also determined in a recent report from the multi-national Financial Stability Board, if regulatory agencies adopt anticipatory approaches, they can more easily reach their regulatory goals. After all, innovation and technology drive evolution, which pose new challenges to regulated entities and regulators, in the global financial system. But through a combination of cloud infrastructure and application program interfaces (API) such new technologies and innovation can also enable real-time monitoring and analysis of historical transaction big data, RegTech will increase data accessibility that can ultimately benefit financial stability. Similarly, our article suggests that by using anticipatory approaches regulatory agencies would be able to more aggressively adapt to changing regulable activities. Indeed, regulator use of predictive analytics could help anticipate future transaction structures, and even forecast future regulation. While these are competing activities, they can serve to converge on predicting early transaction redesigns that are restructured to minimize social costs, thereby averting over-regulation while preserving competitive opportunities.

There are several examples of predictive regulatory methods that can anticipate and reduce RegLag in connection with financial activity at different government agencies.  For instance, the Federal Reserve System’s adoption of electronic payment systems in cryptocurrencies, as dependent on blockchain technology and The FinHub’s efforts to address digital assets at the Securities and Exchange Commission. We also propose addressing market manipulation in securities, derivatives and commodities using the Volatility Index – a popular measure of the expected volatility of the stock market.


John W. Bagby is the Professor Emeritus at Colleges of Information Sciences & Technology and Smeal College of Business, the Pennsylvania State University.

Nizan Geslevich Packin is an Associate Professor of Law, Zicklin School of Business, Baruch College, City University of New York, and an Affiliated Faculty, Indiana University Bloomington, Program on Governance of the Internet & Cybersecurity.


[1] The UDRP exemplifies successful SRO-management as a non-judicial, online ADR method, successfully settling nearly 48,000 cases involving both national and international disputes. See generally, Bagby, John W. & John C. Ruhnka, Protecting Domain Name Assets, 74 C.P.A.J. at 64-69 (April, 2004) accessible at:

[2] SDOs include non-governmental standard development and standard setting organizations whose “rulemaking” governs wide swaths of technical, behavioral and professional norms for which compliance is mandatory by statute or uniformity is supervised by standard bodies. Many SROs also function as SDOs. See generally, Bagby, John W., How Standardization Will Balance Sustainability Goals in the Transport Component of Energy Supply Chains: Efficiency versus Environmental Safety, 54 Transp.J. 136 (Jan. 2015) accessible at:



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