Top Use Cases for Generative AI in Banking, FSI, & Insurance
Customers can now open bank accounts from the comfort of their homes using their smartphones. As the reverberating echoes of groundbreaking innovation draw near, listen closely as we delve into the financial trends of 2024. After knowing all these things, you must have come to know a lot about Artificial Intelligence.
In the UK, for example, Barclays offers an AI chatbot known as “Katie” that answers questions from customers about their banking accounts. Customers can ask Katie any question and receive answers tailored to their account. Chatbots can take on routine tasks by automating simple processes, such as responding to customer inquiries or processing transactions. AI can be used to detect unusual spending, flagging expenditures that fall outside standard patterns or thresholds.
What are the risks of generative AI for banks?
A recent article from Deolitte introduces a UK-based robo-advisor, Wealthify, which is considered one of the fastest growing robo-advisors in the market today. It’s based on an in-house algorithm that recognizes and anticipates changes in market conditions and automatically proposes shifts in clients’ investment accounts, and sends a push notification to the client. Since then, OCR has made its way into enterprise resource planning (ERP) and customer relationship management (CRM), going far beyond check processing. AI finds application in enabling better credit systems by developing a system where lenders can more correctly determine a borrower’s risk with the aid of AI regardless of the social-demographic conditions. But the applications of AI in banking go well beyond cutting down on the amount of manual work. Well, if you can’t think of at least one AI application in finance, you must have been living under a rock.
- Generative AI models have opened new horizons in the finance industry, enabling financial institutions to make data-driven decisions, enhance customer experiences, and drive innovation.
- This kind of segmentation allows for the targeted deployment of resources, such as marketing campaigns, specialized product offerings, or individualized customer care.
- It predicts this future behavior by analyzing past behavioral patterns and smartphone data.
- By tracking regulatory changes and ensuring compliance with laws and regulations, AI can improve decision-making processes and help banks stay up-to-date with constantly evolving compliance requirements.
The integration of AI technology significantly reduces operational costs by automating labor-intensive tasks such as customer service, fraud detection, and risk assessment. This efficiency allows banks to allocate resources more strategically, resulting in substantial savings over time. Practical customer support is an essential part of a successful financial business. Machine learning in the financial industry helps companies meet their customers’ needs with personalized offers and services by analyzing customer behavior in using products.
Ways Artificial Intelligence is Revolutionizing Inventory Management
Generative AI is built from machine learning models capable of presenting well-structured information. This allows banking AI systems to automatically generate financial statements on demand. For example, customers can request customized cash flow or income reports, which the AI compiles into files in seconds. Bankers evaluate several criteria before approving or rejecting a loan application.
AI can be used to power chatbots and virtual assistants that can answer customer questions, provide support, and resolve issues quickly and efficiently. This is one AI for banking use cases that can improve the customer experience and reduce costs for banks. AI is set to revolutionize the banking landscape with the potential to streamline processes, reduce errors, and enhance customer experience.
Transforming customer service in Banking and Finance with AI
So many of life’s necessities hinge on credit history, which makes the approval process for loans and cards important. The financial sector is experiencing swift changes, as seen in the asset management industry. Here, passive fund managers, empowered by AI and algorithms, are challenging the conventional active fund managers. This transformation vividly illustrates AI’s capacity to swiftly reshape industry dynamics.
Thus it remains to be seen to what extent banks that successfully deploy AI strategies materially outperform those that are AI laggards. Issues could also arise as still-new AI regulatory frameworks mature, with the potential for differences to emerge in oversight and requirements across regions. That could affect many industries but will be particularly relevant for the banking sector, which is heavily regulated and faces higher conduct, reputational, and systemic risks than other sectors. It is testament to the benefits of this earlier AI that (despite its complexities) banks, financial service providers, and the insurance sector emerged as some of its most active users. Machine learning in banking, financial services, and insurance accounted for about 18% of the total market, as measured by end-users, at end-2022 (see chart 2). CX solutions in the financial services industry offer companies a chance to not only implement new tools for customer service but also understand the specific needs of their target audience too.
Customer Support: Instant FAQ Resolution
The banking sector has started to use AI and ML (machine learning) significantly, with chatbots being one of the most popular applications. Chatbots along with conversational AI can provide customer support, handle customer queries, and even process transactions. Banks are using chatbots to provide a better customer experience and reduce costs. Every year, billions of dollars are lost due to fraudulent activities, such as identity theft, credit card fraud, and money laundering. AI algorithms can analyze vast amounts of data in real-time, enabling banks and financial institutions to detect suspicious activity and prevent losses.
We find ourselves in an AI-powered digital era facilitated by declining data storage and processing costs, increased accessibility and connectivity, and rapid AI advancements. Banks must adapt to this digital transformation to cater to the evolving demands of their clientele. With AI/ML handling sensitive customer data, banks must prioritize data security to prevent breaches, which could lead to significant reputational and legal consequences. Biases in AI models can result in discriminatory outcomes in lending and other services, posing ethical and regulatory challenges that banks must actively address. Moreover, lacking skilled personnel is a significant challenge to the wider adoption of AI in finance and banking.
Banks have consistently embraced innovative technology to reshape customer interactions. We’re presently in the era of AI-driven digitalization, made possible by reduced data storage costs, improved accessibility and connectivity, and rapid AI advancements. These innovations can increase automation and, when used cautiously after managing risks, frequently enhance decision-making in speed and precision compared to humans. AI technologies like chatbots and recommendation engines provide customers with personalized and responsive services. Chatbots offer 24/7 assistance, resolving queries efficiently, and recommendation engines suggest products and services that align with individual customer needs, resulting in an improved overall customer experience.
Virtual assistants powered by AI technology can interact with customers, providing support and assistance in real time. These intelligent chatbots can handle routine inquiries, account management, and basic transactions, freeing up human resources for more complex tasks. For example, Wells Fargo uses a Facebook Messenger chatbot powered by machine learning to efficiently engage with its customers. Customers can access all the information they require about their accounts and passwords with the help of the chatbot. The use of conversational AI in financial services is transforming customer service by enabling personalized and efficient support. Predictive analytics can identify trends and patterns in customer behavior and forecast future demand for products and services.
Applications of AI in Financial Services
Read more about Top 7 Use Cases of AI For Banks here.
- This empowers your bank to explore new AI use cases, adapt to market changes swiftly, and meet growing customer demands.
- Driven by its intelligent capabilities and a range of automation abilities, AI adoption in banking and financial services sector is on the rise.
- The banking sector, a cornerstone of global finance, is feeling the urgency to modernize.
- One company contributing to this safer and more efficient landscape is viAct, based in Hong Kong.
- A Vectra case study provides an overview of its work to help a prominent healthcare group prevent security attacks.