Home Articles AI and machine learning in identity verification: opportunities and challenges

AI and machine learning in identity verification: opportunities and challenges

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AI and machine learning in identity verification

The role of identity verification in the digital ecosystem is to ensure proof of personality for end-users, thereby guaranteeing safety for both users and the product.

The rise of cutting-edge technologies, such as AI and ML, which have become easily accessible tools on the internet, has given leverage to illicit users to impersonate and breach security measures.

For instance, Infosecurity Magazine recorded a 450% and 410% Deepfake fraud identity increase in both MEA and Latin America, respectively.

We might continue to see an increase in these statistics if products that require stringent security measures to protect and prevent identity breaches do not adapt and evolve accordingly.

AI and machine learning: what you should know?

A simple definition of artificial intelligence is a smart technology that allows computers to make intelligent decisions or perform cognitive tasks similar to humans, while machine learning allows computers to be trained on a set of data to make more efficient decisions.

In identity verification, artificial intelligence and machine learning would be leverage for companies to optimize their checks, especially during KYC processes, and also validate the authenticity of each user.

When AI is integrated into the identity verification process, users experience optimized user interactions thereby cutting off unnecessary delays caused initially by humans. Well this does not mean human interactions or manual checking will be eradicated, it simply means companies won’t have to stay put with human checks as the only validation option.

AI for identity verification

Yes, we are currently in an economy where everything has become digital and most of our data is being stored in the cloud. There are reports of data leakage and confidential information being sold on dark websites to perpetrate evil acts.

Even without data leakages, there are instances where users can be tracked and socially engineered to reveal private data that will make them vulnerable to scams, forgery, and impersonations.

In the era of generative AI, any hacker can easily dub images, make voice clones, and even videos of their target, then this leads to the question of how AI can be leveraged for identity verification.

  • Document Validation: Generative AI can be a leverage for hackers to create PII (personally identifiable information) that they can string up to become a legal tender document to commit synthetic fraud. It takes clever and smarter machines built on trained algorithms such as (LLMs) to easily identify this kind of scam.
  • Biometric Authentication: Users depending on their choice or situations surrounding their decisions can choose from video, voice, or fingerprints as a means of authentication/verification. In order to ensure proof of personality, companies can implement trained AI models dedicated to facial recognition, fingerprint patterns, and live detection tools.

Benefits of AI in identity verification processes

  • Enhanced Security Measures: Have you ever received a mail or notification indicating that a login attempt was made on a certain device, perhaps even including the location? There are complex scenarios surrounding such situations, in which only dedicated security programs with AI implementation can detect them. Consider scenarios like SIM swapping, device cloning, and other intricate backend processes that hackers undergo before attempting unauthorized access. When such attempts are dedicated, decisions can be made by AI to request for 2FA or even prompt the user more to be sure they are real or fake.
  • Improved Accuracy: AI programs are dedicated to making decisions from a large set of data, in the legal sector, 95% of the time, AI has been able to accurately detect legal problems. Narrowed down to identity verification and KYC checks, AI can easily compare information/data supplied by users and make decisions by highlighting which is genuine or fake.
  • Fast Results: Computers generally deliver results fast, therefore using AI to replace a certain percentage of manual checks will go a long way to fasten the verification process. This in return excites the customer as they are not delayed unnecessarily to enjoy the services rendered while ensuring they are who they claim to be.
  • In-Real Time Monitoring: Mind you, compliance requires ongoing monitoring of transactions, as even legitimate users may exploit financial platforms for illegal fund laundering, inadvertently implicating you in AML laws. Integrating dedicated AI programs into your monitoring systems can streamline the process, providing faster and more effective results. AI excels at identifying changes in behavior patterns and enhancing detection capabilities.

What are the challenges of AI today?

  • Poor Data Training: Large language models are fed extensive data, implying that a machine learning model’s efficiency is directly tied to the quality of the data it is trained on. For instance, in the initial stages of Mid-journey, a generative photo AI, there were instances of generated human images displaying an incorrect number of fingers, which is contrary to the natural count of five fingers per hand.
  • Data Bias: AI can exhibit biases when trained on biased data, leading to decision-making models influenced by conditions that reflect societal prejudices and inequalities. This simply means that if this biased data is fed into training models for identity-verifying AI, there would be certain conditions met by illicit users to commit fraud.
  • Robotic Customer Experiences: The use of chatbots is increasing over time. For example, Tidio reports that 19% of companies are currently using chatbots, with 69% planning to implement them. However, there is a significant disparity between human customer support and chatbots. Human support offers a level of dynamism and deep understanding of communication that chatbots currently lack.

Going forward

Companies are increasingly recognizing the potential of AI and machine learning for their operations. According to a report from SAS, 8 out of 10 fraud fighters plan to implement AI by 2025. It’s crucial to acknowledge that malicious actors are constantly seeking new tools to enhance their attacks. Without robust countermeasures, companies risk falling victim to such threats. To ensure efficiency and thoroughness, it is advisable to utilize dedicated AI programs for detecting identity scams, supplemented by human checks for double validation.

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