Fintech industry has been quickly adopting Artificial Intelligence, from automating basic tasks to advanced operations like fraud detection, providing personalised customer service, and delivering strategic financial advice. The adoption of enhanced data analytics, machine learning algorithms and Generative AI has opened up new opportunities, fundamentally improving the operational dynamics of financial services.

JPMorgan Chase uses anomaly detection algorithms to flag unusual transactions or activities that do not fit the customer’s profile. It also uses Natural Language Processing techniques to analyze customer interactions and identify potential fraud.

Klarna, a Swedish fintech company, introduced an AI assistant developed by OpenAI, which managed 2.3 million customer service chats in 35 languages within a month, effectively handling work of 700 full-time agents and simplifying the process of refunds and returns for users through its app.

Betterment, a pioneer robo-advisor, has expanded its offerings over a decade, adding cryptocurrency investing to its sophisticated platform, earning Forbes Advisor’s title as the best robo-advisor.

AI can automate the monitoring and reporting processes that are required for compliance, efficiently scanning transactions for suspicious activities and ensuring adherence to regulatory standards. This automation reduces the risk of non-compliance and lowers operational costs associated with manual compliance tasks.

Compliance and Anti-Money Laundering

In 2019, HSBC introduced an AI system for AML, using machine learning to spot suspicious transactions, cutting review time, enhancing accuracy, and saving $400,000 annually.

ID Finance, a fintech firm based in Spain, is an online consumer lending platform that has credit scoring and risk management system built using sophisticated ML techniques.

AI systems excel in identifying and preventing fraudulent activities in real-time by learning from transaction patterns. They can dynamically adapt to new fraudulent techniques, unlike static rule-based systems.AI can analyse customer behavior, location data, and transaction history to flag unusual activities, significantly reducing false positives and improving fraud detection accuracy.

Fraud detection

AI-driven algorithmic trading platforms analyse large datasets to predict market movements and optimise trading strategies. These platforms use historical and real-time market data, applying complex algorithms to identify profitable trading opportunities.

In trading

In trading, Credit scoring, Fraud detection, customer service chatbots, Robo advisors, Compliance and AML, User authentication

Advanced AI technologies, including biometric and behavioural analysis, provide secure and user-friendly methods for customer authentication. Facial recognition, fingerprint scanning, and voice recognition enhance security while offering a seamless authentication experience. Behavioural biometrics, analysing patterns in device interaction, further improves security by detecting anomalies that may indicate unauthorised access.

User authentication

Leveraging AI, robo-advisors provide personalised investment advice at a fraction of the cost of human advisors. These platforms analyse individual financial goals, risk tolerance, and investment preferences to create customized investment strategies. AI-driven personal finance apps also offer budgeting and savings advice, helping users manage their finances more effectively.

Robo advisors

In lending and credit scoring, AI improves decision-making processes. Traditional credit scoring methods often overlook potential creditworthy candidates as it works on general rule based approach. AI models can build more accurate models for risk assessment by considering non-traditional data points like rental payment histories and social media behaviour. This allows lenders to give loans to wider audience and manage risk effectively.

Credit scoring

AI-powered chatbots offer personalised customer service experiences, available 24/7, without the need for human intervention. These chatbots can handle a range of inquiries, from account balance requests to transaction disputes, learning from each interaction to improve future responses. The cost savings for financial institutions are substantial, as chatbots can scale to handle increasing volumes of queries without additional human resources.

USE CASE - KLARNA