AI, or so-called Artificial Intelligence, is one word that has created a buzz worldwide with its impressive take on all sectors. While technology continues evolving and becoming more embedded in our daily lives, the banking industry has been quick enough to adopt Artificial Intelligence(AI).
AI ML Development can help banks reduce production costs and help make decisions that humans cannot. It also helps to store customers’ data and improve customer engagement. Its algorithms can spot fraudulent information in a matter of seconds.
Some Interesting Facts –
A professional report by Business Insider suggests that the banking and finance industry is rapidly embracing Artificial Intelligence (AI) technology, with nearly 80% of banks aware of its potential benefits.
Moreover, further analysis forecasts that by 2023, banks will save $447 billion via AI applications. These statistics suggest that the sector is headed towards an AI-centric future to enhance efficiency, customer service, productivity, and cost reduction.
Let’s jump into the blog to explore the impact of artificial intelligence in banking sector and learn to use it to enhance operations to maximize profits.
Applications of Artificial Intelligence in the Banking Industry
Artificial intelligence (AI) is becoming crucial to the banking sector, benefits of ai in banking are transforming customer service and how we bank. AI has shown to be a game-changer in industries like fraud detection and customer relationship management.
The ability to deposit and take cash without human help made ATMs the first step in integrating AI into banking. AI is utilized to create virtual assistants, score credit, provide personalized services, and stop fraud and money laundering. Customers can enjoy uninterrupted and efficient service around-the-clock with robots replacing front-office workers.
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Additionally, AI gives banks access to the capability of advanced data analytics, allowing them to manage enormous amounts of data and provide insightful information. Features like AI bots, digital payment advisors, and biometric fraud detection systems make higher-quality services for customers possible.
AI is used in banking in both internal and external applications. AI solutions improve security within an organization and decision-making processes while enhancing client experience and raising banking services’ quality, speed, and accuracy.
Let’s study all the applications of artificial intelligence in the banking sector one by one –
1. Improved Customer Experience
AI technology is transforming the banking industry, providing customers with improved experiences and convenience. It reduces the time taken to record KYC information, eliminates errors, and automates eligibility for facilities like personal loans and credit. AI-based software can reduce approval times for loan disbursement and accurately capture client information to set up accounts without any error, ensuring a smooth experience for customers.
Moreover, AI in banking helps provide improved customer support, offering banking services even on public holidays. This helps to ensure maximum customer retention rates and adds value to the brand. With AI, banks can provide the right services at the right time, enhancing the overall customer experience.
2. Personalized Banking Experience
Personalization is key in the banking industry, where customer experience matters. Banks use AI to gather and process customer data to provide personalized recommendations, offers, and financial advice. This approach helps banks stand out in a standardized market.
Prescriptive personalization uses historical data to create optimized workflows. In contrast, real-time personalization incorporates historical and real-time data for personalized recommendations and customized virtual assistants based on natural language processing models. AI-fueled personalized virtual assistants build trust and activate users while reducing their effort.
Personalized video assistants collect interactive data for further customization or improvements. By utilizing AI for personalization, banks can better understand customers, creating long-term relationships and increasing market success.
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3. Wealth management
In today’s banking industry, customized plans have become a trend, which benefits the banks by increasing their customer base and offers users the ability to manage their wealth efficiently with personalized inputs and advice on risk and investment plans.
With the advancements in AI technology, the banking sector can take advantage of AI-led customer service to meet front-office standards. However, implementing AI-led services can be challenging, especially in countries where diverse language sets are prevalent.
Nonetheless, AI can significantly help in portfolio management, risk analysis, and providing investment advice to customers.
4. Effective Data Collection & Analysis
Banking and finance institutions have to handle millions of transactions daily, generating an enormous amount of data that needs to be collected and recorded accurately. Unfortunately, this task can be overwhelming for employees, and errors can creep in during the process.
This is where AI-based innovative solutions can help by streamlining data collection and analysis. This improves the overall user experience, and the data can also be used to detect fraud and make credit decisions.
These insights are invaluable to financial institutions that need to stay ahead of the game in a rapidly evolving landscape.
5. Chatbots
Chatbots are a prime example of how artificial intelligence revolutionizes the banking industry. With 24/7 availability, they can serve customers around the clock, which is especially beneficial for those needing assistance outside regular business hours.
What’s more, chatbots are constantly learning about customer usage patterns, allowing them to understand and meet the needs of individual users efficiently. By integrating chatbots into banking apps, banks can provide personalized customer support and recommend financial services and products that fit their customer’s unique needs.
A great example of an AI chatbot in banking is Erica, a virtual assistant from the Bank of America. Erica can handle tasks like reducing credit card debt and updating card security. Erica managed over 50 million client requests in 2019, making her a trusted and efficient assistant for Bank of America customers.
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6. Fraud Detection and Cybersecurity
Artificial intelligence (AI) and machine learning are crucial tools in the banking sector to improve online security, detect fraudulent activities, and reduce risks. With the ability to analyze vast amounts of data, AI algorithms can easily detect unusual patterns and flag suspicious transactions for further investigation, leading to a safer and more secure online experience for customers.
For instance, Denmark’s largest bank, Danske Bank, replaced its outdated rules-based fraud detection system with a deep learning algorithm, resulting in a 50% increase in fraud detection capability and a 60% reduction in false positives. The new system also automated crucial decisions while routing some cases to human analysts for further inspection.
Moreover, AI can help banks manage cyber threats, as the financial sector was the most-targeted industry for cyber attacks in 2019, accounting for 29% of all attacks. With continuous monitoring capabilities, AI in financial services can help banks respond to potential cyber-attacks before they affect employees, customers, or internal systems.
While blockchain technology has become a popular solution for fraud prevention due to its transparency and immutability, financial firms still need machine learning-based tools to detect fraud successfully. Banks can incorporate unsupervised algorithms into their systems for fraud detection, which can identify unusual patterns and speed up the review process.
Investing in these technologies will provide customers with a safer and more secure online experience.
7. Risk Management
In today’s globalized world, banking, and financial industries face several external factors that can significantly impact their operations. From natural disasters to political unrest and currency fluctuations, these factors can create a volatile environment where business decisions can be challenging. That’s where AI-driven analytics comes in.
By leveraging advanced algorithms, businesses can gain insights into future trends and potential risks, allowing them to make better-informed decisions.
One area where AI is particularly useful is in evaluating loan applications. By analyzing past behavioural patterns and smartphone data, AI algorithms can predict the likelihood that a borrower will default on a loan.
8. Loan Assessment & Credit Scoring
Banks are adopting AI-based systems to make more informed, secure, and profitable decisions regarding loans and credit. Traditionally, banks have relied heavily on credit history, credit scores, and customer references to assess creditworthiness, but these methods are often flawed and can misclassify creditors.
Fortunately, AI is changing the game by analyzing customer behaviour and patterns to determine creditworthiness for those with limited credit history. AI-based systems also warn banks about specific behaviours that may increase the chances of default, allowing them to make more accurate risk assessments.
At Syndell, we develop AI-based banking mobile applications that allow customers to apply for loans, credit cards, and checkbooks easily.
9. Regulatory Compliance
Banking is a heavily regulated economy sector worldwide, as governments seek to prevent financial crimes and large-scale defaults. This means banks must have acceptable risk profiles and comply with ever-changing compliance regulations.
To manage these challenges, banks often maintain internal compliance teams. However, these teams can be time-consuming and costly to manage manually.
AI uses deep learning and natural language processing (NLP) to read and analyze new compliance requirements for financial institutions. While AI banking can’t replace compliance analysts, it can make their operations faster and more efficient.
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.
10. Predictive Analytics
Have you ever wondered how AI can help your business grow? One of AI’s most common use cases includes general-purpose semantic and natural language applications and broadly applied predictive analytics.
AI can detect specific patterns and correlations in the data, which traditional technology could not previously detect. With AI, businesses can now uncover patterns that indicate untapped sales opportunities, cross-sell opportunities, or even metrics around operational data, leading to a direct revenue impact.
But that’s not all. AI can also use historical data to predict customer behaviour and market trends and identify potential risks and opportunities. By analyzing this data, businesses can make informed decisions about their marketing strategies and sales approaches.
This technology can take your business to the next level by providing insights that may have been overlooked. So, why not try AI and see how it can improve your business?
11. Process Automation
Artificial intelligence is transforming the banking industry using Robotic Process Automation (RPA) algorithms to automate repetitive tasks and increase operational efficiency. RPA allows for the automation of up to 80% of repetitive work processes, freeing up time for knowledge workers to focus on value-added operations that require human intervention.
RPA bots can issue numerous invoices instantaneously, schedule their sending, review payments, secure billing, and streamline collections. Natural Language Processing (NLP) enables bots to extract information and capture knowledge from documents, facilitating application processing and decision-making.
12. Voice Recognition
AI-powered voice assistants are becoming increasingly popular in the banking industry as they offer a convenient and hands-free way for customers to perform various banking tasks. With the help of voice assistants, customers can check their account balances, make transactions, pay bills, and even get answers to their banking-related queries, all by simply using their voice commands.
Voice assistants use natural language processing (NLP) technology to accurately understand and interpret customer requests. They offer a secure way to perform banking transactions, as customers can authenticate themselves using their voice.
With biometric authentication like voice recognition, customers can be confident that their transactions are secure and protected from unauthorized access.
They benefit customers and banking institutions by reducing their workload on customer service representatives, increasing customer satisfaction, and enhancing the overall customer experience.
As technology evolves, AI-powered voice assistants will become an integral part of the banking industry, transforming how we interact with our banks.
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13. Security And Authentication
Facial recognition is a form of biometric authentication that uses AI algorithms to analyze unique facial features to identify an individual. Banks can use this technology to verify the identity of customers when opening an account, accessing their account information, or conducting financial transactions.
Facial recognition provides a more secure and convenient banking experience for customers, eliminating the need to remember complex passwords or worry about personal information being stolen. Moreover, facial recognition can also prevent fraud by detecting attempts by criminals to impersonate a customer using stolen personal information.
Using biometric authentication is a game-changer for the banking industry, as it offers a secure and reliable method of verifying customer identity. As technology continues to evolve, we expect to see more advanced forms of biometric authentication implemented in the banking industry to provide customers with a safer and more convenient banking experience.
Challenges of AI in Banking
Although AI has the potential to revolutionize the finance and banking industry, several challenges still need to be addressed before wider adoption can occur.
1. Data Privacy and Security
One of the primary challenges is data privacy and security. As AI algorithms rely on large amounts of data to train and make decisions, ensuring the privacy and security of customer data is critical. Banks must ensure that sensitive data is encrypted and stored securely to prevent data breaches and other security threats.
2. Ethical Use of AI
Another challenge is the ethical use of AI. While AI can automate routine tasks, it cannot replace human judgment in complex decision-making processes. Banks must ensure that AI is used ethically, transparently, and complies with regulatory standards.
3. Lack of Skilled Personnel
Moreover, lacking skilled personnel is a significant challenge to the wider adoption of AI in finance and banking. The industry requires individuals with advanced analytical skills and technical knowledge to develop, implement and maintain AI systems. Banks must invest in developing and training employees to bridge the skills gap.
4. Cost and Return on Investment(ROI)
Finally, cost and return on investment (ROI) remain significant barriers to adopting AI in the finance and banking industry. While AI systems can increase operational efficiency and reduce costs, the initial investment required can be significant. Banks must carefully assess the costs and benefits of implementing AI systems to ensure a positive ROI.
Examples of artificial intelligence in banking in the real world
Artificial Intelligence (AI) in banking is becoming increasingly prevalent today. We’re seeing more and more financial institutions utilizing AI to improve their services, from streamlining processes to offering more personalized experiences for customers.
To demonstrate this, let’s look at some of the most prominent examples of AI in banking in the real world.
1. Capital One
Capital One is a leading consumer financial services company that uses AI to build customer relationships. The company’s AI-powered Capital One Financial Assistant is a chatbot that helps customers with their banking needs.
Through natural language processing, the chatbot can understand customer queries and provide instant answers.
Capital One has also integrated AI into its credit card fraud detection service, using predictive analytics and machine learning to identify suspicious activities.
2. Kensho Technologies
Kensho Technologies uses AI to help people make better financial decisions. The company has developed an AI-based decision-making platform to give customers personalized investment advice.
The AI-driven platform is trained to recognize financial market patterns and generate strategies to help people maximize their returns and minimize the risk associated with investing.
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Conclusion
As we can see, due to this technology’s numerous advantages, banking and AI go hand in hand. Forbes reports that 65% of senior finance management expects AI and machine learning to improve banking.
Hence, to provide consumers with innovative experiences and top-notch services, all banking institutions must invest in AI technologies.
At Syndell, we work to develop unique AI and ML-based models that help the banking and finance industry improve revenue, cut costs, and manage risks.
Speak with our specialists if you’re seeking AI development services or Hire AI Developers. We can work with you to develop and implement a long-term AI in banking strategy that will meet your needs in the most cost- and technology-efficient way possible.
FAQs
The cost of developing an AI app can vary depending on several factors such as the number of features, resources, and technology integration, as well as the location and agency providing the service. This can range from third-party software solutions to custom platforms developed by in-house or freelance data scientists. Generally, you can expect the cost to be between $40,000 to $180,000.
The expense of developing banking software can fluctuate significantly based on several factors, including the intricacy of the software, the necessary features and functionalities, the proficiency of the development team, and the duration of the development process. The cost of creating a mobile banking app typically falls between $30,000 to $300,000.
Developing a banking app is a complex process that involves a range of tasks such as design, development, testing, and deployment. Depending on the app’s complexity, the number of features required, and the development team’s experience, it can take anywhere from 2000 to 3500 hours to build a banking app.
The duration for app development can range from three to nine months on average, and it largely depends on the complexity of the app and the project’s structure. Different stages of the process require varying amounts of time to complete, with some stages taking longer than others. Typically, drafting a project brief can take around one to two weeks, which is among the more time-consuming stages of the process.
The cost of adding a chatbot varies based on factors such as its complexity, the platform used, and development time. Basic plans on pre-built templates may be low-cost or even free, while custom chatbots can be significantly more expensive. Costs can range from a few hundred dollars to several thousand dollars, so it’s important to work with a reputable development team for an accurate estimate.
