AI in FinTech is revolutionizing the way financial institutions operate, offering enhanced accuracy in risk assessments, automating complex compliance tasks, and providing customers with faster, more personalized experiences.
Artificial intelligence is the technical simulation of human intelligence by computers. AI is trained using data sets to think and draw conclusions like a human, this process is machine learning. It has obvious applications in automated identity verification processes and institutions may use it for improvement to the internal KYC and other compliance processes.
AI offers improved accuracy and speed of customer onboarding. It can be used in many areas, including automated identity verification, biometric matching, and ongoing transaction monitoring. Although AML regulations tend to not include technology specifics, many regulators have confirmed the acceptability of AI and machine learning techniques.
The electronic KYC process allows fin-techs to mitigate regulatory risks at almost any stage of their growth plans. Done right, automated ID verification offers faster and more accurate customer onboarding, which can improve the overall customer experience and conversion.
AI in FinTech
Challenges of AI technology
As with any major change, or adoption of new technology, there are challenges and difficulties associated with AI and machine learning. Staying aware of these is key to minimizing the impact. Working with a reliable technology provider will also help.
The first set of challenges relates to the use of AI techniques and machine learning algorithms in general.
Poor Data Quality or Preparation
AI and machine learning algorithms rely on data for training. Access to historical or training data is needed, and results must be checked. Plus, both quality and quantity of data are needed too. This is more of a concern at the start of using a new system or algorithm since data is limited. Over time and with increased use, the situation improves as more data becomes available.
Avoiding AI Bias
Training needs to be carefully managed and checked to avoid AI bias, which could lead to poor results. AI bias refers to when AI is wrongly trained to reflect human biases. This could be due to human trainers or poor data. The recognition of face as part of the KYC process is one of the areas that are susceptible to this.
Such biases are often not intentional and can be hard to identify. Methods to avoid bias include preparing truly representative data sets, considering outputs rationally, and reviewing AI results against real data.
Human Involvement is Still Needed
AI can only do so much. Human involvement is needed in both the training phase and in real-time usage. During training, human input guides machine learning in identifying failures such as non-matches or fraud cases. These could be real failures or false positives, and algorithms need to learn from this. Also, remember that KYC and AML constantly evolve to meet new challenges and fraud methods. This means that ongoing updates and training for machine learning algorithms are common.
Final Thoughts
Artificial Intelligence, serving as a potent simulation of human cognition, has revolutionized the fintech sector, particularly in expediting customer onboarding through automated identity verification, biometric matching, and transaction scrutiny. While regulatory bodies have started recognizing its potential, integrating AI demands meticulous data sourcing and quality assurance to counter issues like AI bias, a phenomenon where the machine unknowingly inherits human prejudices, especially in delicate areas like facial recognition for KYC.
Notably, while AI’s precision and speed are unparalleled, the technology doesn’t obviate the need for human oversight. Continuous human intervention remains paramount in both training phases and real-time implementations, ensuring adaptability to evolving KYC and AML standards and identifying potential false positives or anomalies.
