FinTech Trends, How AI can mitigate Risks in the Financial Industry?

The recent breakthrough in AI technology will not only have continuing huge impact on Chatbots, but also revolutionize Fraud Detection. The integration of NLP in Fraud Detection, especially in KYC investigation, has made the process more efficient and extensive. In this newsletter, we will first review the traditional KYC process and the challenges for KYC analysts, and then discuss how AI facilitates and even changes KYC process.

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Fraud Detection: KYC investigation

Fraud Detection is another good use case of AI in finance, specifically KYC investigation. The process of KYC (“Know Your Customer”) in Fraud Detection involves a business verifying the identity of its clients and evaluating their appropriateness, as well as identifying any potential risks they may pose.

Here is an example procedure of a KYC process:

Challenges of Traditional KYC Process

The KYC process involves querying, summarizing, and analyzing of large quantity of unstructured data stored in various locations. Many businesses are required to follow KYC procedures, in particular highly regulated industries such as the financial industry. Regulations often require businesses to conduct a variety of checks and screen vast amounts of data. However, performing these tasks manually can prove to be complex, time-consuming, and vulnerable to errors. Besides the challenge of screening all the data, there are many other challenges such as:

1. The customer may have more than one name and a KYC analyst must determine if those different names actually refer to the same person.

2. The same names may refer to two or more persons.

3. Names and news are in different languages.

4. KYC analysts need to determine if the person being investigated has any relationships with other accounts, such as joint accounts or accounts held by family members or business associates.

5. KYC analysts must have knowledge of the fund flow, particularly if there are any questionable patterns or irregularities in it.

Therefore, a solution that can target all the above pain points will greatly improve the KYC investigation process to be faster, more efficient and accurate. The rapid development of AI in finance opens up new potential. By using NPL in KYC investigation, a software can first collect, second structure, third analyze, and forth learn to identify red flags and anomalies from the inputs. Financial institutions and corporations, including importers, exporters and manufacturers, can identify potential risks in business relationships and supply chains more effectively.

An AI-Powered KYC Solution Example: Eureka FinTech

Eureka FinTech, a leading provider of cutting-edge financial technology, has created an AI-powered KYC solution called Eureka. This innovative product helps businesses overcome traditional KYC challenges and improve their overall KYC process. Eureka is a human-centric platform that leverages cutting-edge AI technologies, including NLP, Graph Technology, and Big Data while seamlessly integrating both public data and the client’s internal data to deliver valuable insights. Eureka assists users in analyzing potential risks from business relationships, customers and suppliers that appear suspicious. Very often those risks could not be identified by legacy KYC systems.

Eureka provides a notable advantage over traditional KYC investigation processes, primarily due to its ability to save time and increase efficiency. By leveraging advanced AI technologies, it can rapidly read, summarize, and visualize connections across a vast range of data sources, outperforming human capabilities. Moreover, within a few seconds, Eureka produces Compliance Score reports by examining public domain data, enabling users to rapidly evaluate potential risks. The standard KYC process examines two dimensions: account information and the relationship between accounts, which can be for both individuals and companies. However, Eureka’s 4D-KYC platform takes this investigation further by including two more dimensions: upstream and downstream supply-chain data, as well as tenors of account receivables of supply chain relationship. These additional dimensions are crucial for a thorough analysis of financial fraud.

Eureka makes KYC process simple and fast, even for those who don't have a strong financial or legal background, by automating the process using AI. Eureka uses AI-powered Fraud Detection to innovate in the finance industry by making the KYC process more efficient and accessible to businesses.

Limitations of AI

The use of AI in the financial industry has a remarkable impact, revolutionizing Chatbots, Fraud Detection and many other areas to better serve financial institutions and their customers. However, despite the benefits, there are still potential risks associated with AI usage, such as privacy issue and discrimination risk.

Privacy Issue

Privacy issues are of paramount importance when using AI to collect customer data. While utilizing private data within a bank for internal purposes presents lower privacy risks, financial institutions must strictly adhere to laws and regulations when sharing such information with external parties, as these rules tightly restrict the disclosure of private data. AI can be trained based on private data, but such data must be kept secure to prevent potential data breaches. Though it's worth noting that data breaches aren't solely an AI-related issue. It's vital for institutions to have proper protocols in place to protect sensitive information and maintain customer trust. Protecting customer privacy is of paramount importance, and financial institutions must be unwavering in their diligence when it comes to data security. By doing so, they can effectively leverage the benefits of AI without compromising customer privacy.

Discrimination Risk

Machine learning algorithms have a non-explainable nature like a black box, making them vulnerable to potential biases and discrimination risk. The lack of diversity in the initial training data can lead to algorithmic discrimination, where the model is more likely to produce biased results toward certain groups.

To address this risk, it is essential to implement rigorous testing and validation procedures for AI algorithms to identify and correct any biases or discrimination. Additionally, increasing diversity and inclusivity in data sources is crucial to ensure the training data is representative of the population and avoids any potential biases. Furthermore, involving diverse perspectives in the development and deployment of AI systems can also help identify potential discrimination and biases that might have been overlooked.

Achieving Balance with AI

As the use of AI in the financial Industry continues to grow, it's important to achieve a balance between its benefits and potential risks. Rather than replacing traditional techniques, AI and FinTech tools can work together to create a more efficient and effective system. However, it's crucial to have human oversight throughout the process. Data scientists can help reduce the risk of discrimination by verifying the data and algorithms used, while governments and regulators can implement private data security measures to ensure data is protected. In addition, manual review and investigation can confirm the findings of AI and allow appropriate actions to be taken. Instead of replacing humans, AI should be seen as a tool to support and streamline repetitive tasks, making everyone more efficient and allowing users to handle tasks that would otherwise be impossible. As humans are the creators of AI in finance, we have the power to supervise and guide it to better serve the industry.

Compiled and edited by Yiwen Chen

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