Is AI impacting subsidiary management?

Once upon a time, subsidiary management was a very, very manual thing. Entity managers and governance, risk and compliance officers were drowning in spreadsheets, surrounded by filing cabinets full of contracts and documentation, and scrambling to remember exactly where that director’s name was listed across the group structure so that they could update his home address in the right places lest they be fined by regulators for inaccurate information.

This kind of locally-driven manual maintenance for subsidiary management might’ve been manageable back in the day, but since the global financial crisis brought an increasing amount of global regulatory changes, the number of filings and the need for robust control of the corporate record has grown exponentially. Legal operations teams and those responsible for subsidiary management are finding themselves drowning in data, struggling to create the right reports at the right time and ensure they get to the right place before deadline.

Into this breach has stepped something that was once the bastion of science fiction: artificial intelligence, or AI. But while the AI of cinema involves robots taking over the world, the AI of business analysis is helping to ease the burden of manual reporting. Computers can analyse reams of unstructured data in a fraction of the time it would take the human eye, spotting patterns that we would never be able to see. 

Its applications in subsidiary management are only just starting to be understood, but as entity management technology grows ever-smarter, AI and machine learning are taking leaps upwards in terms of both importance and capabilities. As PWC says in its AI predictions, most executives know that AI has the power to change almost everything about the way they do business, and it could contribute up to $15.7 trillion to the global economy in the next 10 years. 

What do we mean by AI and machine learning?

Before we dive into how AI and machine learning is being used in subsidiary management, let’s first define the two terms - everybody understands them slightly differently, but here’s how we put it.

“The fathers of AI, Minskey and McCarthy, described artificial intelligence as any task performed by a program or a machine that, if a human carried out the same activity, we would say the human had to apply intelligence to accomplish the task,” says ZD Net. This is a fairly broad definition, but, they continue, “today we understand AI to mean systems that typically demonstrate at least some of the following behaviors associated with human intelligence: planning, learning, reasoning, problem solving, knowledge representation, perception, motion, and manipulation and, to a lesser extent, social intelligence and creativity.”

Whereas artificial intelligence is deployed to plan and problem-solve, machine learning is deployed to find and apply patterns in data, says Karen Hao in the MIT Technology Review. “Machine learning algorithms use statistics to find patterns in massive amounts of data. And data, here, encompasses a lot of things - numbers, words, images, clicks, what have you. If it can be digitally stored, it can be fed into a machine learning algorithm.”

How does modern subsidiary management use AI?

Massive amounts of data? Spotting patterns to help make decisions? Sounds like it could be useful in subsidiary management.

And it is. The world of compliance has been turned on its head by the possibilities inherent in AI and machine learning. The systems are proving particularly effective in areas that involve large numbers of documents and repetitive processes, mainly in automating legal, compliance and risk documentation. It has also been useful in analyzing data sets, such as those used to detect Anti-Money Laundering (AML), according to AI specialists IBM

They say AI is particularly useful in both AML/KYC and in regulatory change management to handle things such as:

  • Text analytics and insights, such as processing unstructured data and/or identifying relevant content, negative news and case notes
  • Entity resolution and network analytics, such as determining connections between individuals in order to evaluate risky parties and networks

AI can also be used in regulatory change management for statistical data aggregation, rules extraction and compression, natural language processing, grouping and segmentation, and scenario comparisons. 

In short, AI and machine learning are transforming compliance by helping the increasingly-busy legal operations team to cope with the volume of compliance tasks in modern business. By automating repetitive tasks and workflows, those in charge of subsidiary management can help ensure no deadline is missed and all information is accounted for. By automating document creation, legal operations can become more efficient and streamlined. And by deploying AI algorithms to analyse the corporate record, businesses can gain priceless insights into operations to help steer strategy in the direction of growth. 

Taking subsidiary management to the cloud

In this era where data rules business, AI is becoming increasingly useful and important to make the most of opportunities - but like with anything, it’s a matter of using the right system for your needs, and deploying the right technology to help ease the compliance burden. 

You want to use a subsidiary management system that provides a better customer experience, one that’s intuitive, easy to use and easy to navigate. The Athennian subsidiary management platform has been, and will continue to be, developed in collaboration with its users - people just like you, with similar challenges to solve. 

The cloud-based system has been built to make tracking for legal entities less of a burden, and to enable subsidiary management from anywhere in the world at any time - no need to be in the office, surrounded by the filing cabinets. It’s designed to help you never miss a filing deadline, making it easy to track thousands of filing and compliance events across entities and jurisdictions with automated reminders, email notifications, data reports and more. Automate any corporate document, form or certificate in a single-click using the data you’ve already stored, and manage your templates from one source of truth. 

Discover how we can help your team be more efficient and take back control over compliance; book a demo to see how the Athennian subsidiary management platform uses AI and machine learning to ease the burden of compliance and governance.

About Athennian is the top reviewed legal entity management cloud platform for law firms and in-house corporate teams. Athennian is used by innovative organizations that value modern software with elegant automation and workflows. Integrating entity data management, document assembly, eSign, org charts, and e-file, Athennian is selected by leading law firms and corporate legal and tax teams to scale legal entity governance. Athennian offers rapid migration services for customers from any legacy database including ALF, CorpLink, EnAct, GlobalAct, EnGlobe, FastCompany, Corporate Focus, Blueprint (Diligent Entities), GEMS, hCue, Effacts and more.

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