Why Traditional Compliance is, Frankly, Falling Apart

There are many hours of man-hours spent sifting through vast amounts of documentation. Teams of individuals manually scour through mountains of data. Much time is wasted by cross-referencing information within documents in order to understand complex web of compliance rules and regulation issued by a myriad of local and global organizations. Since the 2008 financial meltdown there has been an exponential increase of rules and regulations on financial services institutions globally. These regulations include traditional Data Protection as well as Anti Money Laundering (AML) / Customer Due Diligence (CDD) together with a variety of other rules globally. Other rules issued by local organizations include the Capital and Reserve requirements on various types of assets, i.e. Life Assurance Policies. There are a multitude of rules in banking, as issued by the Basel Committee on Banking Supervision as well as insurance Solvency II from the EU. The rules above create great complexity together with the numerous reporting requirements around the world, leading to hours of tedious work, resulting in potential for human error as well as employees becoming burnt out as a result of immense pressure put upon them by never-ending deluge of often arbitrary rules, making life extremely difficult for compliance officers and their support teams in banks and insurance companies.

Regulations also change frequently. They can even be supplemented by additional legislation. Because of that, many companies are faced with the huge challenge of keeping up with a huge number of different rules, some of which are quite simple to comply with regards to compliance. In practice, however, such a huge number of processes that have to be executed are always subject to human failure, are characterized by enormous bottlenecks and generate innumerable delays. Needless to say, in almost all cases, such processes are manual in nature. Therefore, they are by definition slow and therefore expensive. On the other hand, of course, such processes are also always quite boring, because there is little fun in a huge number of repetitive tasks, in which no particular creative freedom is granted to those who are responsible for their correct execution.

The AI Advantage: More Than Just Automation

AI brings intelligence to the automation of tasks. Software that uses AI is able to learn, to adapt and to predict. Therefore the mentioned AI regulatory compliance software is able to do many things better, faster and more accurate than humans and even whole teams.

Unpacking the Power of Machine Learning in Compliance

It is important to note that in order to provide the previously outlined functionality AI regulatory compliance software relies on machine learning programs. For example, a company could input their software with all of the documents for a company, including all of the internal policies and procedures for a company, a vast library of all of the laws and regulations that apply to a company, as well as prior audit reports from previous years as well as news articles detailing enforcement actions taken against other companies as well as how those actions affected other companies. The machine learning programs then go through all of this information identifying patterns to help the AI system learn what good compliance looks like and what bad compliance looks like. Machine learning is able to recognize patterns in data from past events, enabling the software to recognize potential risks for a company before they actually become a problem for the company.

A good example of how AI can be used to support the work of a banking organization’s compliance team is in relation to money laundering. The detection of money laundering involves the analysis of huge numbers of individual transactions, including both those that are domestic and those that are international. However, the vast majority of individual transactions that are analyzed will be found to be completely normal. As such, they will not require any further analysis. The role of the AI system is to be able to identify individual transactions that are in some way unusual. It is true that a human can also look for obvious indicators of money laundering. However, the AI system is able to identify patterns within the data that would otherwise go completely un-noticed. An example of this would be a sequence of apparently unrelated individual transactions. However, upon closer inspection, the AI system could identify that these individual transactions, when taken together, represent the movement of money and the subsequent attempts to disguise the true origin of that money. In the insurance industry, AI can also be used to support a wide variety of different regulatory requirements. For example, the solvency requirements placed on insurance organizations are constantly changing. As a result, insurance organizations are required to update their internal policies on an ongoing basis in order to ensure that they are always fully compliant with current legislation. In addition to this, an AI system can also be used to support organizations that are subject to a large and varied number of different laws. For example, an AI system could be used by a retail bank to support its compliance with anti-money laundering and know your customer requirements. It could also be used by an insurance company to support its compliance with a variety of different laws including, but not limited to, the solvency requirements placed on insurance organizations.

Predictive Analytics: Seeing Around Corners

The most exciting feature of the AI in compliance software is the predictive analytics functionality. This type of functionality will enable companies and banks to address future issues proactively and timely in order to prevent any future compliance related penalties and any resulting damage to a company’s brand. This type of system will use historical data and current information and also proposed legislation that has not yet been implemented. The system will then warn the end user of potential future compliance related issues that will arise due to future changes to current laws that will come into force several months later.

Another question, how many ways can an AI predict problems and avoid them. A concrete example could be the following: An AI recognizes in advance potential changes for future updates to the laws and regulations (e.g. required procedures for processing customer data). For example, the AI can detect changes to the laws and plans in the legislative proposals in about 6 months time for the processing of consumer data by insurance companies. The AI then compares this with the current processes for processing customer data in a company X and recognizes a gap that has to be closed before the law change comes into effect in order to avoid sanctions etc. As a result, necessary changes to processes are carried out in time before the law change comes into effect.

AI Regulatory Compliance Software for Banking and Insurance: A Game-Changer

For banking and insurance companies, who are amongst the most heavily regulated industries globally, there is an enormous challenge in staying compliant with a massive volume of relevant regulations and corresponding penalties for non-compliance.

Revolutionizing Risk Management in Financial Institutions

For financial institutions the regulatory environment can be a minefield of often complex legislation. Anti-Money Laundering (AML), Know Your Customer (KYC), sanctions screening, market abuse are just a few of the examples of areas that require strict compliance. However, often different pieces of legislation can be in conflict with one another and so cause confusion. Traditionally many banks and other financial institutions have relied on a manual check of information in order to complete their compliance requirements. Unfortunately, however, this type of check is inherently slow and often prone to error, potentially allowing complex fraud schemes to slip through the net.

AI compliance software for banking and insurance sectors addresses the entire panorama of challenges encountered by financial institutions when it comes to complying with the huge array of requirements issued by bodies like the anti-money laundering authority and the market abuse regulator. The AI compliance software is thus designed to be a holistic solution for financial institutions, providing them with all the tools and functionalities that are necessary for them to comply with all of the relevant laws and requirements in a timely and efficient manner.

KYC/AML Enhanced: This module processes all of the data the bank holds on its customers. It classifies individual customers and their correspondents on a scale of low to high risk of fraud. During the day the software analyzes all transactions in real time to detect suspicious activity. This can be a sudden change in behavior by a customer or a single isolated unusual transaction. The software can even use unstructured information such as articles in the news to complete a more in-depth risk profile of all customers. Unlike manual monitoring of watchlists for named individuals that are already known to be of risk to banks around the world, this software monitors all activity and is at the cutting edge of preventing fraud and is a key benefit that our compliance software can bring to banks.

Fraud Detection: The ability to identify anomalies in behavior that may suggest someone is trying to commit a fraud thereby allowing action to be taken to prevent the fraud before it can cause damage.

As the volume of financial transactions increases so does the amount of reports that need to be submitted to regulators. AI can automatically gather the relevant information from within a financial institution and organize it in a format ready for submission to the relevant bodies. In some cases the AI can even complete the first draft of a report saving hours of work for the financial institutions’ compliance teams.

A bank dealing with millions of transactions a day cannot possibly check a fraction of these for potential compliance breaches. In such cases the bank can make use of the ongoing compliance checks performed by the AI. The results are then analyzed by the compliance department and where necessary steps are taken.

Streamlining Compliance for Insurance Providers

Many aspects of insurance require adequate and efficient management in order to meet the wide range of regulatory requirements. This applies to aspects such as the underwriting of policies, the processing of claims as well as the protection of customers’ personal details. It is therefore essential that the insurance company is able to manage and process a large amount of data efficiently. In particular, the requirement to manage risks within the insurance company as well as holding sufficient capital in order to meet any future claims, are key issues that the insurance provider must comply with in line with Solvency II requirements.

However, for the insurance sector, there are some special features of the AI regulatory compliance software:

Policy Compliance: When the AI is analyzing a policy it can also make sure that all current rules and regulations have been taken into account when writing a new policy or reviewing an existing one for updates. It can even highlight unclear wording or parts of the policy that may cause problems in inconsistencies in how the policy is to be implemented.

For fraud, insurance companies can use the same type of information and processes as banks to detect and prevent money laundering and the fraud of others. This could include tracking down individuals who attempt to commit insurance fraud and prosecute them for their actions.

Data Privacy: Many countries in the world have enhanced rules around the use of personal information and its use by companies, including insurance companies. The AI can identify information that is classified as sensitive personal information and then the software can assist the company in monitoring and managing the use of such information to ensure that it is used in compliance with all applicable privacy legislation such as the GDPR in Europe and the CCPA in California in the US. As an example, a single data breach of customer information could be potentially catastrophic for an insurance company.

Market Conduct: enables an insurer to monitor its sales practices, advertisements and customer communications for potential market conduct violations. Such conduct breaches could result in a severe financial penalty as well as serious damage to an insurer’s business and reputation in the market place.

The Human Element: Empowering, Not Replacing

A related to Banking and Insurance transactions is whether AI can replace Human existence in financial services, and so far I have seen the opposite enable Human existence to focus on analysis, problem solving and decision making/taking action where there are no rules predefined to follow. In a Banking environment, this would mean that a compliance officer’s work would transform from a manual data intensive job to that of a super analyst in his/her field of expertise.

FAQ

What exactly is AI regulatory compliance software?

This is a broad term used to describe software that contains elements of artificial intelligence including Machine Learning and Natural Language Processing. This type of software can be used in a wide variety of industry sectors, but is most applicable to the Banking and Insurance industries, where the amount of rules and regulations to be managed on an ongoing basis is too great for a challenge for any human to manage alone. It automates repetitive tasks and can also analyze data to forecast future problems that may occur.

Is this kind of software only for huge corporations?

Is this kind of software only for huge corporations? No. It is very possible to implement it in small banks or insurance companies. Many solutions are very flexible and scalable. It allows even small businesses to compete with big players.

How does AI help with new regulations that pop up?

How will AI / software help deal with changes to regulations? The software can be trained up to look at on going changes to regulations, updates to legal databases, news feeds etc. It will then go through these new changes and compare them to existing practices / policies and highlight areas for change.

Can AI really understand complex legal jargon?

Yes, actually! This type of software that helps to read legal jargon and to process huge volumes of data to extract required information using Natural Language Processing (NLP) and other AI technologies. NLP can be used to read documents and contracts to extract key information and to identify obligations and to summarize complex clauses.

What are the main benefits for the banking sector specifically?

What are the main benefits for the banking sector specifically? A: For banks, the biggest advantages of benefits will stem from using the AI to implement and manage rules for Anti-Money Laundering (AML) and Know Your Customer (KYC) checks. It will also help them to manage and detect instances of fraud as well as to provide them with a means to complete and submit all necessary reports in due course, processing all the relevant data in the process of time to highlight instances of potential risk that could be missed by human computation.

And for the insurance sector?

What benefits do insurance companies receive from using AI regulatory compliance software to manage risk across their business? A: There are many very large and far-reaching benefits for insurance companies, including policy compliance, the detection of insurance fraud including instances of fraudulent claims, data privacy (as per the General Data Protection Regulation or “GDPR” or similar elsewhere around the world) and fair market conduct.

Is it expensive to implement AI compliance software?

The costs for an AI compliance software mostly consist of an initial investment. These costs, however, are often less than the costs for non-compliance with regulations, such as the payment of fines, the damage to a company’s name and to other legal costs and expenses. But there are also big savings potential due to the automatization of big amounts of data.

Conclusion

With such an enormous volume of data being used to support compliance there is certainly going to be an ever increasing need for software solutions that can process all of this information on behalf of compliance officers. And whilst there may be an initial feeling that the volumes of regulations are going to reduce, it is clear that there is certainly going to be an increased focus on compliance and, therefore, the need for software to aid with this ‘fight’ will be rapidly increasing. Using old manual methods to fight this ‘battle’ is going to be extremely difficult, close to impossible. We clearly need a super powered ‘soldier’ to fight this ‘battle’!

So, there you have it, embracing AI in compliance is smart business, it will protect your organization from massive fines, safeguard your brand’s reputation and make life so much easier for your compliance teams by turning them into super-analysts that tackle complex strategic problems and make the critical human judgment, while the AI will be busy monitoring and analyzing the vast amounts of data that you require to ensure that your organization remains fully compliant. So, let’s have a serious look at how we can utilize AI within our compliance function to ensure that our organization remains at the forefront of regulatory compliance and that our brand continues to thrive globally.