AI in fintech: use cases, solutions,
trends & implementation challenges

AI-driven credit scoring models analyze hundreds of data points - from traditional credit history to alternative data like bank account or investment account data - to determine customer creditworthiness and build high-precision risk profiles. This allows lenders to identify subtle correlations within data that rule-based systems miss, accelerating the credit assessment process and enabling fairer credit decisions and more inclusive lending practices.

• Determine who qualifies for a loan
  based on customer data from credit histories, tax returns, business balance sheets, and other sources.
• Define a suitable credit limit
  by estimating customers’ disposable income and actual ability to make regular loan payments.
• Calculate a competitive interest rate
  based on risk scores and market trends to maximize profit without alienating the customer base.

Using machine learning and deep learning algorithms, financial software can process data streams in real time to uncover non-linear patterns that rule-based systems would otherwise struggle to capture, allowing them to interpret and forecast complex market trends. Furthermore, ML systems continuously learn from new data and adapt to evolving economic conditions to maintain predictive accuracy. This enables financial firms and private investors to continuously fine-tune their trading and investment strategies and maximize profits.

• Predict stock price fluctuations
  and market volatility based on global financial trends, companies’ financial statements, or investor sentiment on social media and optimize portfolio investments accordingly.
• Provide automated financial advice and assistance
  with financial planning and portfolio management operations through robo-advisors.
• Enhance algorithmic trading
  with machine learning to expand the range of rules and factors considered when automatically executing trade orders.

Banks and other financial organizations are increasingly relying on artificial intelligence to meet customer expectations for more intuitive and tailored services, establish meaningful connections with clients, and improve their engagement. AI-driven personalization goes far beyond simple segmentation rules, with systems designed to interpret each customer’s tone and sentiment across interactions to offer relevant and context-aware assistance.

• Launch targeted marketing campaigns
  to engage potential and current clients with personalized ads, maximizing lead generation and minimizing churn.
• Provide clients with product and service recommendations
  tailored to their financial goals or assist users with transactions and other operations via chatbots, virtual assistants, and other conversational AI tools.
• Seamlessly validate transactions or access sensitive information
  in digital banking or trading apps through voice-based or computer vision face authentication.
• Facilitate financial product design
  by generating product concepts tailored to the needs and preferences of specific market segments thanks to GenAI-based assistants.

Solutions powered by NLP, generative AI, and machine learning algorithms support context understanding, automating both repetitive tasks like data entry and complex financial and accounting workflows. Unlike basic bots, agentic AI solutions can resolve exceptions like report inconsistencies and data gaps, significantly reducing human error in P&L and bank reconciliation statements.

• Automatically compile P&L statements
  summarizing the revenues and expenses of a company based on data combined from multiple financial reports.
• Streamline bank reconciliation
  by cross-checking the balance of a bank account with that reported in the most recent bank statement to spot inconsistencies.
• Automate accounts payable and receivable,
  including invoice generation, sending of payment reminders, and financial data validation.