The fintech sector is constantly evolving, with technology playing a pivotal role. Recent years have seen technologies such as cloud computing, Artificial Intelligence, and blockchain drive a new era in the financial sector. AI has been influential in helping financial companies solve complex problems, such as approving loans and stopping fraud. In this post, we will investigate future innovations that will leverage AI’s capabilities to bring the much-needed change in the fintech sector.
Fraud Detection Systems
Fraud has disrupted traditional services in the fintech industry for years. This industry has become a hotbed of fraudulent activities, with customers advised to keep digital vigilance and defend themselves against online threats. Some of the most common fintech frauds include using bots to create fake accounts, phishing scams, and hacking customer accounts.
While proactive measures such as creating strong accounts and using two-factor authentication (2FA) are recommended, these security solutions can sometimes be vulnerable. For this reason, fintech companies have begun using AI-supported fraud detection systems to analyze large datasets from varying sources, including transaction history and account activity. This allows them to identify suspicious patterns and behaviours quickly.
It’s also worth noting that AI fraud-detection systems often adopt and improve their learning capabilities. These systems learn from past security incidences to become more effective and efficient in identifying and preventing potential fraud activities. AI security systems such as voice recognition and face/fingerprint identification provide more security than traditional passwords.
Predictive AI-Powered Analysis
Fintech companies are constantly analyzing and collecting data in a bid to beat the competition. This data can help banks and other financial companies provide specific customer needs and manage potential risks more effectively. For instance, a company can assess data to know customer transaction behaviours and identify new growth opportunities before making data-backed decisions.
As you read this post, a new data analytics era is unfolding, thanks to Artificial Intelligence. This technology allows financial service companies to process large volumes of data quickly. These companies exploit AI capabilities to make more accurate insights regarding future trends and behaviours. AI-powered data analytics tools can quickly browse and analyze unstructured data like social media comments and customer reviews.
In addition, generative AI services such as ChatGPT can support data analysts in multiple ways, including:
- Interpreting and generating complex codes for new scripts.
- Curate personalized learning plans for different topics, such as complex math problems.
- Summarize vast data and create actionable recommendations.
- Test existing business strategies by creating challenging situations.
- Translate technical data findings into understandable language.
Efficient Customer Service
Customer service in the financial sector is among the first departments to enjoy the improved capabilities of AI. Many companies use AI-powered chatbots in their customer service centers to provide efficient support. These chatbots are highly trained to simulate human conversations with customers, although some bots use natural language processing (NLP) instead of AI.
So, what’s the value of incorporating chatbots in fintech customer support? These technological innovations can help customers find information instantly without speaking to a human agent, which can take a few hours. They use sophisticated languages to provide free-flowing conversations with end users and even automate relevant tasks.
It’s also important to note that modern AI chatbots use natural language understanding (NLU) to comprehend open-ended user input. For example, you want to play live blackjack but cannot deposit funds using your credit card or e-wallet due to a technical glitch. In that case, you can contact customer support, with most operators using AI to provide this service in multiple languages and improve the experience.
Helping Regulatory Compliance
Businesses in the financial sector deal with substantial regulatory compliance requirements. For big financial companies, mapping out compliance activities and associated risks can be a material concern. So, naturally, these businesses are constantly looking for ways to maintain compliance requirements without spending top dollar.
But is AI the answer they have been searching for? Yes, this technology is currently playing an active role in fintech regulatory compliance through catalyzing innovation and efficiency. By harnessing its pattern recognition and data processing capabilities, AI is helping many businesses quickly navigate the confusing web of global regulations.
Now consider this: AI can continuously monitor multiple activities like employee training participation and qualification. It will then proceed to highlight areas where the fintech company risks non-compliance. For example, AI will automatically flag down and report an employee who doesn’t have mandatory compliance training. This can prompt the company to initiate immediate corrective action and avoid possible penalties.
In addition, AI systems continuously analyze and monitor regulatory databases and official government announcements or updates. So, let’s say a government has introduced new anti-money laundering (AML) rules. The AI system will immediately alert the relevant team for institutional compliance. This proactive approach will significantly help reduce potential fines or bans associated with non-compliance.
Credit Qualification Analysis
The advent of AI technology in the finance sector couldn’t have come at a better time for credit departments. Traditionally, banks and other financial service providers use historical data to analyze creditworthiness. Unfortunately, this can only go as far as determining credit risks partially.
On the other hand, AI-powered credit score analysis collects a broader set of data to help the company make a more informed decision. This data can include the customer’s transaction history, social presence, reputation with other institutions, and more. Put simply, an AI-powered credit analytic tool provides a more holistic risk assessment report. In the evolving landscape of fintech innovations, AI-powered tools such as Portrait Pal are poised to redefine financial technology by optimizing customer interactions and enhancing operational efficiency.
Modern AI systems can also analyze the customer’s investments, credit accounts, and cash to help the company know their financial health. These organizations use Large Language Model (LLM) to asses data and determine any risk factors involved. Moreover, depending on the client’s profile and demands, fintech companies can customize their loans, insurance policies, and other services.
Conclusion
AI is already leaving a mark in the fintech industry despite making notable breakthroughs in the past decade. By incorporating AI in their services, fintech companies will enjoy increased speed and efficiency in customer experiences and decision-making processes. The future of AI in the fintech industry is unquestionable.
