AI in RegTech

AI & Machine Learning: Going beyond regulatory penalties

Traditionally, financial institutions resist in innovating their compliance due to its complexities. If a new ML approach is identified and fails, regulators charge a penalty, and if the new approach succeeds, regulators can again react with hefty fines (due to the unregulated practices). So the financial firms are restrained from regulatory innovations in the past but times are changing now. Regulators encourage and sometimes even mandate to innovate. Amongst the immense pressure of the regulatory fines and reputational damage, firms are being pushed for new regulatory innovation with machine learning and artificial intelligence. The timing is opportune to deploy machine learning for all these regulation-based validations, which can cut $80B out of the $100B annual spend in compliance.

The critical focus for Machine learning in the financial industry is AML and KYC. To be precise, the focus will impact the effective risk screening. Machine learning and the natural language processing models, implemented on the trusted historical data will streamline and increase the visibility of the risks with all forms of screening, payment filtering, and transaction monitoring. One such use case includes automating workflow, aggregating and reduplicating data, or enriching the entities can help risk screening and the investigations more effective, but at the end it will be 80% more efficient screening process that will reduce the size of the battlefield, allowing us to win the war with illicit activities.

Financial Firms Embracing ML faster than anticipated

When we initiated our R &D in 2016, there was more uncertainty about machine learning in regulatory technology, but in the present time, the world is traveling towards embracing ML much faster than we anticipated.

Regulators’ primary focus is on risk-averse, but not tech-savvy or innovation, and thus constraining from the usage of machine learning systems. The actual reality is regulators are attentive, forward-looking, already evaluating the machine learning systems in production, and encouraging sandboxes for ML models.

The regulators released last year about their vision towards promoting innovation in the financial crime compliance area. AI and digital identity are the future of the compliance industry, and regulators are encouraging sandboxes with the aid of technology providers and re-inventing from failures during the pilot programs. This approach is trending across the globe with regulatory agencies like FCA, FINMA, SEC, SEBI, MAS instituting regulatory sandboxes and FinTech programs to hasten the deployment of innovative RegTech solutions.

Few of the banks are experimenting with artificial intelligence and digital identity technologies applicable to their AML compliance programs. These innovations and technologies strengthens the transaction monitoring systems with innovative approaches to further the efforts of protecting the financial system against the illicit financial activity.

A Natural Language Processing search engine provides regulators with search capabilities to quickly and accurately identify the required information. The next-gen AI platform depends on rule-based analytics, which can process the enormous information from datasets provided by regulators. It also uses a combination of business rules, statistical models, machine-learning analytics and other AI-based algorithms to detect data anomalies.

Innovation has the potential to enhance the aspects of AML compliance programs in financial firms, such as transaction monitoring, suspicious activity reporting, and risk identification. Some firms are becoming increasingly sophisticated in their approaches to identifying these suspected activities and in taking measures to prevent the risk profiles.

Will Automation cause chaos?

Yes, there will be increased efficiency in risk models, and with it, industry chaos due to automation. AI and ML will drive more efficient risk screening tools and automate the regulatory process workflow. Firms will be spending more time on analyzing the risk rather than investigating the meaningless data from the result of risk screening.

‘It’s human to err, but not the tech.’

Few traditional industrialists opinionate that automation and machine learning isn’t going to solve the evolving challenges in money laundering, but it’s the manual process that is defective, not automation. We at Sensiple is driving regulatory compliance towards a holistic approach of end-to-end RegTech by automating more regulatory process workflow and the risk screening with ML-powered regulatory automation. To know more about the implementation and innovation, talk to our experts

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AI in RegTech
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Insights Solutions
  • 27 Jan, 2020
  • 4 Mins read

Traditionally, financial institutions resist in innovating their compliance due to its complexities. If a new ML approach is identified and fails, regulators charge a penalty, and if the new approach succeeds, regulators can again react with hefty fines (due to the unregulated practices). So the financial firms are restrained from regulatory innovations in the past but times are changing now. Regulators encourage and sometimes even mandate to innovate. Amongst the immense pressure of the regulatory fines and reputational damage, firms are being pushed for new regulatory innovation with machine learning and artificial intelligence. The timing is opportune to deploy machine learning for all these regulation-based validations, which can cut $80B out of the $100B annual spend in compliance.

The critical focus for Machine learning in the financial industry is AML and KYC. To be precise, the focus will impact the effective risk screening. Machine learning and the natural language processing models, implemented on the trusted historical data will streamline and increase the visibility of the risks with all forms of screening, payment filtering, and transaction monitoring. One such use case includes automating workflow, aggregating and reduplicating data, or enriching the entities can help risk screening and the investigations more effective, but at the end it will be 80% more efficient screening process that will reduce the size of the battlefield, allowing us to win the war with illicit activities.

Financial Firms Embracing ML faster than anticipated

When we initiated our R &D in 2016, there was more uncertainty about machine learning in regulatory technology, but in the present time, the world is traveling towards embracing ML much faster than we anticipated.

Regulators’ primary focus is on risk-averse, but not tech-savvy or innovation, and thus constraining from the usage of machine learning systems. The actual reality is regulators are attentive, forward-looking, already evaluating the machine learning systems in production, and encouraging sandboxes for ML models.

The regulators released last year about their vision towards promoting innovation in the financial crime compliance area. AI and digital identity are the future of the compliance industry, and regulators are encouraging sandboxes with the aid of technology providers and re-inventing from failures during the pilot programs. This approach is trending across the globe with regulatory agencies like FCA, FINMA, SEC, SEBI, MAS instituting regulatory sandboxes and FinTech programs to hasten the deployment of innovative RegTech solutions.

Few of the banks are experimenting with artificial intelligence and digital identity technologies applicable to their AML compliance programs. These innovations and technologies strengthens the transaction monitoring systems with innovative approaches to further the efforts of protecting the financial system against the illicit financial activity.

A Natural Language Processing search engine provides regulators with search capabilities to quickly and accurately identify the required information. The next-gen AI platform depends on rule-based analytics, which can process the enormous information from datasets provided by regulators. It also uses a combination of business rules, statistical models, machine-learning analytics and other AI-based algorithms to detect data anomalies.

Innovation has the potential to enhance the aspects of AML compliance programs in financial firms, such as transaction monitoring, suspicious activity reporting, and risk identification. Some firms are becoming increasingly sophisticated in their approaches to identifying these suspected activities and in taking measures to prevent the risk profiles.

Will Automation cause chaos?

Yes, there will be increased efficiency in risk models, and with it, industry chaos due to automation. AI and ML will drive more efficient risk screening tools and automate the regulatory process workflow. Firms will be spending more time on analyzing the risk rather than investigating the meaningless data from the result of risk screening.

‘It’s human to err, but not the tech.’

Few traditional industrialists opinionate that automation and machine learning isn’t going to solve the evolving challenges in money laundering, but it’s the manual process that is defective, not automation. We at Sensiple is driving regulatory compliance towards a holistic approach of end-to-end RegTech by automating more regulatory process workflow and the risk screening with ML-powered regulatory automation. To know more about the implementation and innovation, talk to our experts

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