Kangaroo Court

Kangaroo Court: Taking Compliance from Insurance to Strategic Advisory

Share this article

Start thinking about your data as a capital asset. This is not an original idea. Douglas B Laney from Gartner built a strong argument around this concept in his book Infonomics: How to monetize, manage, and measure information as an asset for competitive advantage. The past decade has witnessed enormous growth of AI and ML solutions from start-ups and skunkworks teams within organizations. The economic opportunity of AI globally has been predicted at anywhere between $15-$17 trillion USD by 2030. This figure represents the application of various technologies within the AI umbrella, and covers a broad net of industries and business functions. However, and perhaps more exciting, this projection does not account for the innovations that will emerge through the breaking down of data silos and transformation of enterprise data into a true source of intelligence and growth.

To clarify, information is the product of processed data. Data by itself is the collection of digital material that has not been sorted into some form of digestible content. Once processed we have information which can be searched. The idea here is straightforward and directly analogous to mining. Raw materials are extracted from the earth, but they are not valuable until they have been processed into their final product. You start with Iron Ore, and you end up with the steel frames that form the Sears Tower (no one in Chicago calls it Willis). For all intents and purposes, data goes on the same journey. The challenge many organizations face is one of coordination and education. Neither can be achieved without executive and board support for enterprise-wide artificial intelligence (AI) and machine learning (ML) initiatives for finding new sources of value. Critically, this task must extend beyond obvious first choice avenues for innovation, like sales and market analysis.

The digital innovation of front-end functions is nothing new, and any organization worth its weight should already be introducing AI solutions as a core strategy to focus resources and increase growth. However, the value within legal and compliance data is often misunderstood and rarely realized. As a basic example, regulatory technology (regtech) is an emerging area of digital technology that aims at making regulatory compliance easier and, where possible, automated. Regulatory bodies across the world are faced with the numerous challenges of monitoring the events in the industry they oversee to ensure balance with the help of rules and associated laws. The entities that come under the jurisdiction of these regulators have the obligation to keep track of the industry changes and maintaining regulatory alignment. A failure to comply may lead to financial penalties, disrepute, and in extreme cases, severe litigation. The challenge for the regulated is keeping track of regulations in real time, whilst documenting their process in a defensible manner.

The full potential of the compliance function can only be achieved with investment towards regtech and monitoring solutions that provide near to real time analysis across both structured and unstructured enterprise datasets. To be successful, there must be a commitment by the executive team to shift thought leadership from considering the role of compliance as an insurance function, to reimagining it as a source of strategic analysis and dynamic risk assessment. The regulators themselves have the obligation to monitor all the entities that fall under their jurisdiction to ensure that their regulations are followed and there is no exploitation of customer or other entities. Ultimately, they must verify the compliance of these entities either by checking their documentation or process. Constantly monitoring an ever-growing number of entities and individuals can be difficult, given limited resources in terms of both people and funding. By encouraging innovation within the cost centers of organizations, entities can deliver value both internally and for the regulators themselves.

Despite the numerous start-ups and increased academic research into progressively more exciting forms of deep learning and cutting-edge analysis, there needs to be a better incentive plan that encourages board support and executive leadership to invest resources towards enterprise-wide compliance and regulatory AI. One possible pathway could be tax incentives for firms that can demonstrate skunkworks initiatives and resource commitment to enhancing the digitally intelligent growth of their data ecosystems. This could also be in the form of partnering with academic research institutions or start-ups looking to solve the most significant challenges that prevent AI and ML from achieving its full potential. Whichever way organizations are incentivized, the ultimate purpose behind introducing these tools is to reduce waste and increase the growth from both the front-end and non-obvious avenues like data rich compliance functions.

To catalyze breakthrough growth, leaders must set bold aspirations, make tough choices, and mobilize resources at scale. Simply put, the ability to develop, deliver, and scale new products, services, processes, and business models rapidly is a muscle that virtually every organization needs to strengthen. There is a positive correlation between innovation and financial performance. This can be achieved most conspicuously in two areas. First is the ability to set a bold yet plausible aspiration for innovation that is grounded in a clear view of the economic value that innovation needs to deliver. And second is the ability to make tough resource-allocation choices about the people and funds required to seize innovation’s value at a scale sufficient enough to make a difference. This requires an organizational commitment to long-term value creation.

Advances in digital analytics have transformed the way businesses operate. While strategy development will always require creative and thoughtful executives to set aspirations and make bold choices, the right application of AI within the legal function can give leadership an edge. This can be realized by reducing bias in decision making by calibrating the likelihood of a strategy succeeding before resources are allocation at scale. Compliance and risk functions specialize in looking for anomalies that spell trouble. It makes sense to give this expertise room to breathe and innovate in the form of strategic risk advisory working towards the same ultimate goals. If they do not evolve beyond insurance policies, the value potential of their expertise will never be fully realized.

Organizations can also unearth new growth opportunities by complementing traditional brainstorming methodologies with various forms of pattern recognition. Breaking down the data silos across an organization is a critical first step to achieving this goal. Without communication and innovation from the compliance and risk function, it is impossible to say that management has the full story. Consider the growth of M&A from private and public investors. By enabling the knowledge of compliance and risk functions through increased access to AI and ML solutions, private equity firms can run health checks on their newly acquired entities to understand the critical issues that must be addressed early to maximize the likelihood of a clean exit (and minimize loss).

Identifying early-stage trends by mapping the real-time data of how various business lines are evolving can help the executive team make significant moves before their competitors. Executive teams would benefit from knowing how associated trends are evolving. They could gain these insights through real-time tracking of regulatory changes, monitoring customer sentiment, and identifying risks to product or service delivery. Unknown risks are present within most organizations, but they can be significantly reduced by taking the role of information governance and enhancing its value as an analytics function. In a world of increasing uncertainty, companies need to be dynamic in how they set and manage their digital initiatives.

The imperative for a strategic AI centered approach is universal, yet some companies are already leading the pack through better overall capability, talent, leadership, and resource allocation. All of this can be linked to better outcomes. Given the resources and tools available today, it is simply not good enough to leave traditional cost center functions in the realm of rule advisory, reactive risk assessment, and basic data administration. The pandemic has dramatically increased the speed at which digital is fundamentally changing business. At the same time, the pandemic has created new vulnerability to – along with new opportunities from – future disruptions. In order to ensure organizations have reduced exposure to cyber-attacks, enterprise inefficiency, and regulatory risk, the legal function must have resources to remain ahead of the game. Catching up with leaders will become increasingly difficult given the top economic performers have already taken more actions than peers to achieve their AI goals.

To leave legal and compliance functions outside of enterprise intelligence projects would be a grave mistake for the organization of tomorrow. Similar to how law schools are increasing their students’ exposure to new and emerging technologies, organizations should seriously consider the role of engineers within traditional cost center roles. The challenges that new and expanding forms of data create are only increasing. More than ever, firms need the ability to move proactively and quickly determine whether they build or buy. There is a wealth of hidden potential that can only be unlocked if legal functions have the support to build the intelligent ecosystem of tomorrow.

Learn more about ACEDS e-discovery training and certification, and subscribe to the ACEDS blog for weekly updates.

Chip Delany on Email
Chip Delany
Strategy Director at Lineal Services, previously worked as a strategist for Legal AI tech firm NexLP and before that as a consultant in continuous improvement and labor modelling. Australian National and US permanent resident.

Share this article