What is artificial intelligence (AI) helping banking professionals achieve? 

Here are some key applications and benefits of AI in the banking sector:

1. Customer Service Improvement: AI-powered chatbots and virtual assistants that can handle digital banking inquiries 24/7, providing instant responses to common questions and freeing up human agents for more complex issues. 

How do artificial intelligence-powered chatbots work? These particular AI-powered tools are trained language models that, when asked a question, quickly search a prefilled database of company policies and frequently asked questions (FAQs) to give account holders around-the-clock access to help.

2. Fraud Detection and Prevention: AI systems analyze transaction patterns in real time to identify anomalies that may indicate fraudulent activity. This helps banks and credit unions respond more quickly to potential threats, reducing the risk of financial losses.

How does artificial intelligence support fraud detection and prevention? Consider these AI tools like hiring a team of investigators that can work 24/7, never sleeping and never tiring, and with amazing pattern recognition skills. These bots are trained on a set of rules that the financial institution and their fintech partner have predetermined, then they assist the institution in identifying anomalies and fraudulent behaviors.

3. Risk Management: AI is used to better assess risk in lending by analyzing a wide range of data points, including some that may not be immediately obvious to human analysts. This can lead to more accurate credit scoring and underwriting.

How does artificial intelligence assist with risk mitigation? Similar to fraud prevention and detection, AI tools can be programmed to speed up determination processes, such as loan approvals. These tools are trained on the requirements that financial institutions have determined keep them operating in a safe way, and can sift through data inputs fairly quickly, allowing loan officers to spend more time interacting with the people they are serving.
Beyond loans, AI-based algorithms can be used by risk management teams to monitor cash flow, market conditions, and investments to ensure their practices are compliant with the law.

4. Personalized Banking: AI enables banks and credit unions to tailor products and services to individual account holders based on their behavior and preferences. This personalization can enhance customer satisfaction and loyalty.

How can artificial intelligence support personalized banking? Similar to the introduction of computers to the business world decades ago, digitization of information so that it is easily searchable and sortable created many opportunities for customer service and marketing teams. 
Now, in the age of artificial intelligence in banking, tools like predictive analytics and financial services marketing automation are super-powered by the ability to micro segment audiences by their financial behaviors. With that knowledge, building campaigns that are attractive to account holders not by demographics but by transactions gets closer to the core of who they are, their unique priorities and life stage. 

5. Operational Efficiency: AI automates routine tasks such as data entry, claim processing, and compliance checks, which can significantly reduce operational costs and improve efficiency.

How can artificial intelligence assist with operational efficiency in banking? Imagine spending less time reviewing rows of data across multiple source inputs, juggling multiple business goals and cross-departmental needs, along with regulatory and compliance requirements.
Artificial intelligence in banking can support an operation’s ability to bring siloed data sources together, find connections between these sources, and unveil business opportunities and strategies with a level of accuracy that unfortunately, the human mind may not be able to do alone. With the help of these tools, marketing and operations teams are elevated, and can spend more time on strategic and interpersonal work.

6. Investment and Wealth Management: AI can support wealth management teams in banks and credit unions in analyzing large volumes of financial data to make predictions about market trends, helping to optimize investment strategies.

What do artificial Intelligence tools do to support investment and wealth management teams? Core data, credit card data, call center or teller interactions, SMS and email data, call lists, appointment calendar technologies…all of this siloed data can and should be used to support finding customers or members who have funds available for wealth and investment products. AI tools can support these teams by analyzing data sources, tracking interactions, and attributing conversions across a variety of digital and physical touch points.

By integrating artificial intelligence in banking into their operations, banks and credit unions can not only enhance their efficiency and customer service but also stay competitive in an increasingly digital financial landscape.

Additional Resources from Alkami

• To learn more about stopping silent attrition using artificial intelligence in banking,  watch Alkami’s on-demand webinar here
• See how Capital Credit Union put artificial intelligence in banking to use at their credit union maximizing retention, engagement and revenue.
• Navigating the Regulatory Landscape of Artificial Intelligence in Banking
• Artificial intelligence in banking has strong adoption by “data-first” FIs 

Frequently Asked Questions About Artificial Intelligence in Banking:

Q.What is artificial intelligence in banking?
A. Artificial intelligence in banking refers to the technology used by financial institutions to simulate human cognition in the analysis, decision-making, and customer and member interaction processes. It encompasses various technologies including machine learning, natural language processing, and robotics to enhance the efficiency and effectiveness of banking services.

Q. How does artificial intelligence in banking improve customer service?
A. AI enhances customer service by powering chatbots and virtual assistants that provide 24/7 customer support, personalized financial advice, and quick transaction handling. It can also analyze transaction data to tailor services and offers to individual needs, improving overall satisfaction.

Q. What are the security benefits of using artificial intelligence in banking?
A. AI improves banking security through advanced fraud detection systems that analyze patterns in transaction data to identify suspicious activities. Additionally, AI-driven biometric systems enhance identity verification processes, making banking transactions more secure.

Q. Can artificial intelligence in banking help with risk management?
A. Yes, AI plays a crucial role in risk management by processing large volumes of data to forecast potential risks and market trends. It aids in credit scoring, underwriting, and compliance monitoring, allowing for more accurate risk assessments and proactive management strategies.

Q. How does artificial intelligence in banking contribute to cost reduction?
A. Artificial intelligence in banking contributes to cost reduction by automating routine tasks such as data entry, compliance checks, and customer queries, which reduces the need for manual labor and speeds up operations. This automation not only cuts costs but also increases efficiency and allows human employees to focus on more complex tasks.

Q. What challenges do banks and credit unions face when implementing artificial intelligence?
A. Challenges include high initial investment costs, data privacy and security concerns, and the need for skilled personnel to manage AI systems. Additionally, there is a challenge in integrating AI with existing banking systems and ensuring that AI decisions are transparent and compliant with regulatory standards.

Q. Are there ethical concerns with using artificial intelligence in banking?
A. Yes, ethical concerns include issues of data privacy, potential bias in AI algorithms, and the transparency of AI decision-making processes. Banks and credit unions need to address these concerns by implementing data governance practices and ensuring AI systems are regularly audited for fairness and accuracy.

With contributions by Director, Product Management (Data & Marketing) Mark Leher