EN
With the implementation of the EU AI Act providing a regulatory framework for ethical and responsible AI adoption, banks and financial institutions are starting to pay close attention. What exactly is AI used for in modern banking? Let's dive into different use cases and what it means for banks.
Want to know more? Learn about EU AI Act and how to prepare.
AI-driven virtual assistants have become important tools for delivering personalized customer experiences in banking apps. These AI assistants leverage natural language processing (NLP) and machine learning algorithms to understand customer queries and provide relevant responses in real time.
By analyzing vast amounts of transaction data, including transaction history and spending patterns, AI chatbots can offer tailored product recommendations and provide financial advice. Statistics indicate that AI-powered chatbots can handle up to 80% of routine customer inquiries, significantly reducing response times and improving overall satisfaction rates.
Do it right: Fargo AI and customised financial assistant
Wells Fargo bank uses its AI assistant within the banking app to help customers tackle their daily financial tasks. Powered by Google Dialogflow and PaLM 2 LLMs, Fargo offers personalized recommendations tailored to users' financial needs and goals, suggesting suitable products and services such as savings accounts, smart budgeting, credit cards, or investment opportunities.
Unlike AI assistants in PFM platforms, chatbots are usually designed to ease the workload and streamline the customer service. They instantly respond to customer inquiries, resolve issues, and offer personalized recommendations. AI chatbots can understand and interpret customer queries in real-time, delivering seamless and efficient service round-the-clock. Additionally, AI chatbots can analyze customer interactions to continuously improve responses and enhance the overall customer experience.
According to Juniper Research, AI-powered chatbots can reduce customer service costs by up to 30% and improve response times by 99%, which makes them a valuable tool for any bank or fintech that wants to automate the internal processes and streamline communication.
Do it right: AINO and customer service automation
DNB, one of the biggest banks in Scandinavia, is using their AINO chatbot to manage their high volume of chat traffic and help its customer centres with a “chat-first” strategy. Using automation and AI technology, DNB managed to automate over half of all online chat interactions in 6 months.
AI-driven automation is employed across various banking operations, such as account management, transaction processing, and customer service. Through machine learning algorithms, repetitive tasks like document verification and data entry are automated, reducing manual workload and improving efficiency.
AI-powered smart search features enable users to quickly find relevant information within the app, such as transaction history, and account details, or even help with app navigation and transaction searches through natural language processing and contextual understanding.
Do it right: Finn and smart banking app search
Dutch fintech bunq uses its GenAI platform Finn within the app similar to ChatGPT. Offering a chat-style text box, users can interact with the assistant and ask about various topics, such as their bank account information, current spending patterns, location of specific services or features and other financial inquiries. All in real-time.
By leveraging advanced machine learning techniques, AI platforms analyze a wide range of data sources, including payment history, credit utilization, and behavioural patterns, to generate more accurate risk assessments.
In this particular case seen below, AI algorithms excel at processing large volumes of transaction data to identify predictive patterns and trends. Studies have demonstrated that AI-based credit scoring models can reduce default rates by up to 30%, enabling banks to make more informed lending decisions and minimise credit risks.
Do it right: Goldman Sachs and AI-driven simplification
Global investment company implemented an AI platform to streamline the processing of critical documents essential for regulatory reporting and compliance adherence. A key component was also automating the review process of Qualified Financial Contracts (QFCs), aligning with the mandates of the Dodd-Frank Act. The integration of AI technology enabled Goldman Sachs to enhance operational efficiency and accuracy in regulatory compliance procedures.
PFM tools, boosted by AI assistants, are helping users take control of their finances and make smarter financial decisions. Banks usually use these platforms in cooperation with external providers like TapiX that leverage AI algorithms to analyze transaction data. With enriched data, PFM platforms can categorize expenses, and provide personalised budgeting recommendations or investment guidance.
Research suggests that users of AI-powered PFM tools save, on average, 15% more than those who do not utilize such platforms, highlighting the significant impact of AI on promoting financial wellness.
Do it right: Deutche Bank and smart investment
Leveraging AI algorithms, the bank analyses customer portfolios to pinpoint risks and propose tailored adjustments. Notably, AI algorithms draw insights from analogous customer portfolios to offer personalized product recommendations, ensuring a unique experience for each client. Portfolio adjustments are made by prioritizing substantial benefits while weighing potential gains against associated costs. Ultimately, human advisors leverage AI insights to make well-informed decisions aligned with individual customer preferences and objectives.
Did you know? High-quality data are essential for the proper use of AI. Read our take on why.
.webp)
Michal Maliarov
Senior insider
