As businesses expand through online platforms and cross-border sales surge, the need for strong compliance increases. Regulators and firms are embracing big data and better analytics to ‘regulate, protect consumers, and assert trust in governance in the online world’.

This paper discusses analytics and compliance policy practices, data quality, privacy, interoperability challenges, and case studies both from outside of the financial industries and within the financial industries. It also discusses how legal tech entrepreneurs are creating scalable compliance solutions for small and medium enterprises (SME`s) that will be able to internationalise more efficiently.

The cost reduction, increase in detection of risks, and validation of credibility of organisations are driven by quantitative data. It is argued that analytics ought not to be considered merely a practice of governmentality but as a competitive factor in the digital economy.

Introduction

Digitalisation has transformed the compliance space. With the proliferation of data flows and the integration of algorithmic governance into the business practices of global enterprises, compliance is now not merely a matter of auditing after the fact, but has become a proactive and immediate endeavour. Policymakers are unable to strike a balance that promotes innovation, accountability, and structures that allow it to grow and protect consumers.

Analytics specifically has transformed this space. Analytics helps in creating a picture of risk, identifying areas of concern, or even creating predictive models to alert regulators and firms of possible risks before they are realised (Janssen & Helbig, 2018).In other words, rather than being a static retrospective process, compliance is transforming into a more fluid, adaptive, and evidentiary process.

But it is not without its challenges. Issues about the quality of input data, ethical issues because of mass monitoring, and decentralised legal regimes raise concerns for careful design. Strong governance and linking analytics to business goals are necessary to ensure that the analytics support rather than detract from compliance.

Best Practices in Data-Driven Compliance

Data governance is the first step toward successful analytics implementation in the policymaking dimension of compliance. But predictive models can only be developed on the basis of reliable, representative, and ethically obtained data sets (Mittelstadt, 2019). Analytics, without them, runs the risk of reinforcing biases or producing false outputs.

Cooperation between sectors has also been demonstrated as key. Public Private Partnership (PPP) arrangements allow researchers access to private datasets that would be unavailable to regulators. Cooperative analytics platforms have led to better risk identification in AML enforcement and have reduced the cost for firms of complying with AML requirements by up to 20% (Zetzsche et al, 2020). Predictive analytics takes the ability to regulate even further by moving the role of regulator from a reactive enforcement to a preventative role, especially in the areas of cybersecurity, trade monitoring, and consumer protection.

Entrepreneurial Innovation: Legal Tech and Scalable Compliance

Much of the discourse has occurred at the regulatory and large corporate level, but legal tech startups are beginning to define the compliance space. Legal tech entrepreneurs are using analytics to create compliance platforms that will be scalable for implementation by SMEs without the prohibitive costs of tailor-made systems.

London-based ComplyAdvantage, for example, has created a compliance program that uses AI to screen clients and transactions against a global database of risks. SMEs utilising the platform also highlight the ability to onboard customers in a quicker, yet compliant manner, with a decrease of as much as 40% of the manual compliance work ComplyAdvantage, 2021). In the same line of thought, Singapore’s Tookitaki deploys machine learning applications for financial and non-financial sectors through modular solutions that grow along with the business.

These entrepreneurial innovations show that compliance analytics are more than mechanisms for the avoidance of risk, but rather also help further growth into markets. Through lower entry thresholds, legal tech platforms give SMEs more and better tools to internationalise, as compliance can be aligned with expansion strategies.

Expanding Case Studies Beyond Finance

While the financial services industry has, in many ways, been a pioneer in compliance analytics, other industries are just as informative. In the field of health care, European hospitals have used predictive analytics to comply with regulations to protect patients’ data privacy under the GDPR. For example, one hospital found that tracking who accessed records and how, decreased the incidence of inappropriate access by 35% over the course of a year (Voigt & Von dem Bussche, 2017).

As it relates to global e-commerce, Amazon and Alibaba use machine learning to identify counterfeit products and uphold intellectual property rights. Such systems protect not only the trust of consumers but also cut down the costs of dealing with disputes, which have been reported to account for savings of more than $100 million every year just in fraud expenses (OECD, 2020).

In the energy and extractives sectors, African businesses are using satellite and sensor data technologies to ensure compliance with environmental regulations. This enables firms to anticipate potential violations before they occur, reducing fines, but also enhancing relationships with regulators and host communities. These broad industry examples are useful in that they indicate applications for compliance analytics that stretch well beyond finance, and show the versatility of compliance analytics in supporting governance within any industry.

Quantitative Benefits: Efficiency and Cost Savings

The power of compliance analytics is grounded in empirical evidence. Many organisations using automated compliance services report an average cost decrease of 30% according to another survey by PwC (2021), which also indicates that 60% of these firms experienced a decline in costs. In much the same way, Deloitte (2020) documents how the use of AI for monitoring and surveillance “improves monitoring by increasing the accuracy of detecting anomalies by 50 per cent, as well as decreasing false positives that often suck compliance resources.”

The efficiencies are even greater for SMEs. Automated dashboards monitoring customs’ risks at the ports of entry also minimise delays on shipments, decreasing average clearance times by 25% and saving thousands of dollars in possible penalties. These figures show how compliance analytics reduce regulatory risk but also create measurable business value.

Challenges of Integration

Barriers still exist despite the benefits. Access can be constrained by data silos, a lack of standardisation, and the use of donor-funded or external platforms. There are also ongoing privacy concerns, as collecting large amounts of data, even for compliance, can become much more of a surveillance culture than there used to be between oversight and surveillance. On top of that, harmonising compliance analytics would require not only technical agreements but also diplomatic understanding between countries, as there are differences in regulations across borders as well (Klievink et al., 2017).

Conclusion

Data-driven compliance has transitioned from the margins of regulatory practice to become a core element of digital governance. It increases efficiency, decreases costs, and increases trust of stakeholders in its adoption. Legal tech tools have allowed small and medium enterprises to scale and affordably comply using data-driven analyses, transforming a previously costly regulatory burden into a competitive strength. Compliant analytics are relevant across a variety of sectors – including healthcare, e-commerce, and energy – with proven, measurable benefits such as decreased fraud and quicker clearance by regulators.

The difficulty for policymakers is ensuring the analytics take place inside governance systems that respect rights, allow for interoperability, and support innovation. For business, the opportunity lies in reframing the narrative of compliance from a cost to a competitive advantage. In this sense, data analytics not only helps companies operate within the confines of the digital economy –small and medium enterprises, or SMEs, as well– but also helps them to do so.

Author: Zita Agwunobi is a Business Data Analyst, Technology and Compliance Attorney, Management Analyst professional with over 15 years of experience.

References

ComplyAdvantage. (2021). The state of financial crime 2021. https://complyadvantage.com/insights/the-state-of-financial-crime-2021/

Deloitte. (2020). AI and compliance: The next frontier. Deloitte Insights. https://www.deloitte.com/global/en/Industries/financial-services/perspectives/ai-next-frontier-in-investment-management.html

Janssen, M., & Helbig, N. (2018). Innovating and changing the policy-cycle: Policy-makers be prepared! Government Information Quarterly, 35(4), S99–S109. https://research.tudelft.nl/files/85677451/1_s2.0_S0740624X15300265_main.pdf

Klievink, B., Romijn, B. J., Cunningham, S., & de Bruijn, H. (2017). Big data in the public sector: Uncertainties and readiness. Information Systems Frontiers, 19(2), 267–283. https://link.springer.com/article/10.1007/s10796-016-9686-2

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PwC. (2021). Time to get serious about compliance analytics. PwC Global. https://www.pwc.com/gx/en/about-pwc/global-annual-review-2021/downloads/pwc-global-annual-review-2021.pdf

Voigt, P., & Von dem Bussche, A. (2017). The EU General Data Protection Regulation (GDPR): A practical guide. Springer. https://link.springer.com/book/10.1007/978-3-319-57959-7

Zetzsche, D. A., Buckley, R. P., Arner, D. W., & Barberis, J. N. (2020). The rise of fintech: Risks and regulatory responses. International Journal of Financial Regulation and Compliance, 3(2), 1–23. https://www.tandfonline.com/doi/full/10.1080/23311975.2020.1725309