Using AI to Combat Laundering of Money

Combatting laundering of money is the significant challenge for the financial services industry. AML (Current laundering of anti-money) is the process of compliance which is dominated by the high level of repetitive, manual and task of data-intensive that are unproductive at disrupting the activity of money laundering.

Assumed that low effect of current efforts of AML is combined with the complexity that is increasing of growing volume and threats of the data that have to be analyzed and explore the competencies of artificial intelligence which has the potential in order to support a change in capability of AML and provide with the means in order to adapt the threat of modern laundering of money.

In spite of of the potential awareness is increasing and there is a number of AI applications which have exploded the debate on the efficiency on certain explanations and the degree to which AI should be reliable and replace the analysis of human and decision making. While there is attention towards implementation of AI which is easy to comprehend and brain of a human is doubtfully the system which is unpredictable in existence. The sentiment is growing that may combine the insight of human and process with AI that can drive outcomes in a better way and different ways of working that are effective than arranging the isolation of AI or humans.

In order to realize and explore AI potential the services of financial industry need to continue and build the understanding of the risks, limitations and capabilities of AI and establish the framework which is ethical through which the use and development of AI could be governed and impact of these models that are emerging could be trusted and proven.

Opportunity and need for modification in AML
Currently,both the approach of industry and controlling framework are laboring in the fight which is against the laundering of money. Today laundering of money around the world is calculated around 2% to 5% of GDP. There are many resources which are deployed by institutions of finance that may combat laundering of money, the present approach is not delivering accurate results. Agreeing to the report by Europol there is 10% of the reports of transactions by service financial institution that may lead towards the investigation by authorities.

The companies of financial services may struggle to contain the cost of compliance and the industry may expect the volume of data and the treats to further continue. As the result, there is an increase in the need for the acknowledgment and innovation in AML with acceptance of the modern technology.

Impact and capability of AI rising rapidly
As the capabilities of AI have increased in the recent years it is shown by the examples of real life such as virtual robotics and assistants, the transformative potential which has gathered the thoughts of business that may seek towards to decrease cost and manage the threat, increase the productivity. Annual venture investment of capital into the startup of AI US has increased by the expected six-fold since 2000. There is regulatory compliance that has not escaped the investors. 238m pounds of venture capital is invested in firms of RegTech in the year 2017. Industry bodies and regulators such as the financial authority of conduct and the financial services of stability have acknowledged the prevalence which is growing the AI services of finance and the applications of compliance.

Addressing the charge of compliance with AI
AI could drive the competences in the operational hotspot such as customer persistence which is due, a transaction the controls of monitoring and screening. AML monitoring of transaction may control and generate the high level of the alerts that are positive. The issue of the cost is amplified by the ineffectiveness in process of investigation that may create the divide which is significant between the employed efforts and impact of monitoring control of the transaction.

AI may offer the opportunities that may reduce the cost of operations with no disadvantage by introducing the techniques of machine learning at some phases of transactions process of monitoring. AI may also increase the diligence of customer and screen the control by using the processing of language and methods of text mining.

Integrated KYC Bringing back context into AML with AI
There is an understanding of how AI would be integrated and applied with the activity of human that may drive towards new thinking. It may open up opportunities that may lead towards the shift in approach to knowing the customer. In a wave of transformation integration of risk, valuation will be observed, investigate, monitor and process of due diligence that may help AI to break and provide the basis for determining and detecting the activity of risk.

In the future, AI could increase scale, breadth, and reviews of KYC that may integrate the monitoring and screening the analysis. Detection and risk models would learn and assess from a set of efforts and outcomes of both behavior and profile of the customer. By leveraging the learning capability attached to investigators that are skilled and model is used to augment operations and provide the control to train the resources that are new.