Investment in Artificial Intelligence (AI) recoups in a short while through improved efficiencies. CIOs in regulated industries reap returns using AI to cut compliance costs.
Regulations are a part of doing business, especially for companies in the financial and health sectors. Most businesses have to comply with many regulations, which are in place to protect consumers and markets. But many regulations are complex, time-consuming, and costly. And the complexity and challenges keep on increasing over the years. Deloitte estimates compliance costs for banks have increased by 60% since the financial crisis of 2008.
Many businesses do not comply inadvertently. Still, they face huge fines and end up with negative publicity. Artificial intelligence (AI) and intelligent automation help businesses meet compliance obligations effortlessly.
1. RPA and NLP track regulations
Most businesses in most countries have to comply with several regulations. In many cases, they may even have to change or tweak their business strategies to comply with regulations imposed by the state. The recent issue of China slapping tech-giant Alibaba with a $2.8 billion fine for violating anti-monopoly regulations, is a case in point.
In the US, financial institutions process up to 300 million pages of new regulations every year. These regulations devolve from the state, federal, and municipal authorities. The situation in other sectors such as healthcare and food is not much different.
Tracking regulations and co-opting it to business processes is a complicated and time-consuming task. The frequency changes in regulations makes the task even more challenging. Most businesses operate under the constant shadow of non-compliance with some rules.
Enter robotic process automation (RPA). Programming RPA to identify new regulations and keep track of changes improves compliance. It speeds up the process and eliminates slip-ups. RPA completes in minutes, which takes manual analysts days of painstaking research.
Using RPA for compliance involves three broad steps:
- Using Optical character recognition (OCR) to transform regulatory materials into machine-readable texts.
- Using Neuro-linguistic programming (NLP) to process the texts and make sense of the contents.
- Developing AI models to process the NLP output and offer policy change options. The AI models feature algorithms trained in previous cases of similar nature. It filters through new regulations and flags the regulations relevant to the business.
2. Streamlined and timely reporting
Filing reports make up the core of compliance. But reporting is a time-draining, resource-intensive, and non-value-adding activity. The process involves aggregating and compiling information into reports of specified formats. Analysts must review and interpret thousands of regulations to retrieve the relevant data for the reports. Mistakes in the reports, delayed filing, or missed submissions equate to non-compliance. The company becomes susceptible to fines and other structures.
AI-powered algorithms parse unstructured regulatory data and identify reporting requirements. The algorithm interprets the requirements based on previous situations. The software code triggers automated processes that access company resources and auto-generate reports.
3. Data protection and integrity
Several businesses store and use customers’ personal information. The onus is on these companies to ensure such sensitive data does not fall into unauthorized hands. RPA enables processing sensitive data without manual intervention. The possibility of data theft, misuse, and even genuine errors reduce. It also leaves an audit trail, making it easy to track user activity and identify rogue insiders.
RPA bots eliminate human errors from data handling and maintain the integrity of data sets.
4. Spruce up transaction monitoring
Many industries, especially finance, have transaction monitoring systems for real-time compliance. These systems validate transactions by applying rules and flag violations. US Banks, for instance, have to see if transactions violate OFAC regulations.
Regulated sectors, such as banking, need stringent workflows to ensure efficient compliance management.
Compliance systems err on the side of caution. Traditional rules-based transaction monitoring systems often produce thousands of false positives daily. Sometimes, false positives touch 90%. Each error requires review by a human compliance officer. The enormous volumes of false-positives add to the costs and tie up human resources for non-value generating activity. Integrating Artificial Intelligence into transaction monitoring systems automates and refines the process.
With AI, instances of false positives reduce, and no transaction slips through the crack. The AI engine resolves issues and escalates only high-risk issues to the compliance officer. The ML algorithms learn from compliance officers’ data and get better with each transaction.
5. Validate and execute marketing material
A big area where compliance applies is in marketing materials. Companies have to make sure they follow specified ethical standards and do not mislead customers. For instance, pharma companies must ensure compliance with drug labels and regulations. Vetting and approving new marketing materials to ensure compliance is burdensome. It delays roll-out when time is critical to gain first movers advantage.
The trend toward personalized marketing content drives up compliance costs and effort. Each piece of content has to go through compliance checks.
Businesses have been toying with Artificial Intelligence to overcome these challenges. AI algorithms:
- Scan content and confirm compliance in double-quick time.
- In advanced use-case, AI bots edit and write regulation-compliant marketing copy.
- Reduce HR costs. Adding human resources to scale marketing strategies often results in considerable cost increases. At times, the compliance costs make the marketing effort unviable. Unlike humans, AI algorithms work round the clock without tiring, do not need leaves, and do not need motivation to put in their productive best.
6. Perform background and legal checks
Many establishments, especially banks, have long-term relationships with customers. Such establishments must perform due diligence to ensure customers are law-abiding. Many criminals have used financial institutions for money laundering and other illegal transactions. These clients drag the institutions down with them, and saddle them with heavy fines and other structures.
Traditional background checks are resource intensive and time-consuming. The process involves collecting documents, checking databases, and reviewing media outlets. Artificial Intelligence and automation can streamline the process and make it much more effective than before.
Bots crawl through the web and unearth any mention of the client under review. Sentiment analysis flags negative content about the client. NLP technologies scan court documents for signs of illegal activity and relevant media mentions.
The evolving regulatory landscape renders traditional compliance and risk analysis methods obsolete. The conventional techniques are incapable of processing the enormous volumes of data that exist in today’s age of Big Data. Most enterprises also have many compliance mandates, making these tasks onerous. Yet, any lax will lead to the business ending up on the wrong side of law enforcement. Depending on the severity of the non-compliance, companies will face fines or even have top executives imprisoned. In most cases, the collateral damage of eroded brand image will be more significant, and recovery, if at all, will take a long time. A robust AI-powered compliance framework enables enterprises to pre-empt such situations.
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