29 June, 2024 Technology
Developing and deploying Enterprise AI at scale requires a new technology stack.
Enterprise AI is a category of enterprise software that harnesses advanced artificial intelligence techniques to drive digital transformation. Developing and deploying Enterprise AI at scale requires a new technology stack.
It helps make data-based decisions on inventory and marketing. AI is being used extensively by banks, governments, financial institutions, and others to prevent fraud. Even giant corporations like Nike are using these kinds of AI management techniques to improve the management of their expansive organizations. Artificial Intelligence (AI) has made significant strides in streamlining operations and workflow within businesses across various industries. By integrating AI technologies, companies can automate repetitive tasks, optimize resource allocation, and improve overall efficiency. Artificial intelligence is a pivotal technology in digital transformation, enabling businesses to scale and innovate. AI and machine learning are leveraged by digital transformation platforms to analyse social, historical, and behavioural data, providing a comprehensive 360-degree view of members’ needs.
It is useful for companies to look at AI through the lens of business capabilities rather than technologies. Broadly speaking, AI can support three important business needs: automating business processes, gaining insight through data analysis, and engaging with customers and employees.
AI is used in banking to enhance efficiency, security, and customer experiences. It automates routine tasks like data entry and fraud detection, reducing operational costs. AI-driven chatbots provide 24/7 customer support. AI for corporate banking automates tasks, boosts customer services through chatbots, detects fraud, optimizes investment, and predicts market trends. This increases productivity, lowers costs, and provides more individualized services.
Robotic process automation (RPA), software that mimics rules-based digital tasks performed by humans, is being applied in banking to eliminate much of the time-intensive and error-prone work involved in entering customer data from contracts, forms, and other sources.
Coupled with improved handwriting recognition, natural language processing, and other AI technologies, RPA bots become intelligent process automation tools that can handle an increasingly wide range of banking workflows previously handled by humans. This definition of hyper automation explains in detail the benefits of combining AI and RPA.
1. Chatbots on call:
One of the biggest benefits of AI in banking is the use of conversational assistants or chatbots. A chatbot, unlike an employee, is available 24/7, and customers have become increasingly comfortable using this software program to answer questions and handle many standard banking tasks that previously involved person-to-person interaction. Business customers might not be aware of merchant services and loan offerings that can help resolve payment or credit issues. Supported by predictive analytics and AI tools like and machine learning, chatbots (and customer service agents) can make the right offer on the right device in real-time, delivering highly personalized service and potentially boosting revenue. Financial services companies and customers should keep in mind that despite breakthroughs in natural language processing and natural language generation, most commercially deployed chatbots are “comparatively simple.” They can deal with straightforward queries, but by and large, they are unable to understand context. That doesn’t negate their value.
2. Improved fraud detection and regulatory compliance:
Fraud detection. Fraud detection is an area where machines are “genuinely superior to people,” Bennett said.
“They can crunch vast amounts of numbers, applying different algorithms. They don’t make mistakes unless they’re badly programmed,” she said. Humans have a habit of making mistakes, especially with repetitive tasks.”
Before the pandemic, U.K.-based Bennett said she could be in a different country every day for work. Her credit card company’s fraud detection had gotten so good that her card was never declined as she traveled from one geography to another. The one instance when there was a fraud — someone tried to buy a computer as she was buying cheese in Madrid — she was contacted immediately.
“What I’m saying is that companies with well-structured, good data have already been able to put AI to good use in detecting fraud,” she said. As companies improve their data collection and algorithms become more advanced, the benefit to financial firms is growing.
3. Regulatory compliance:
Banking is one of the most highly regulated sectors of the economy, both in the United States and worldwide. Governments use their regulatory authority to make sure banks have acceptable risk profiles to avoid large-scale defaults, as well as to make sure banking customers are not using banks to perpetrate financial crimes. As such, banks must comply with a myriad of regulations requiring them to know their customers, uphold customer privacy, monitor wire transfers, and prevent money laundering and other fraud, and so on.
Banking regulatory compliance has significant costs and even higher liability if not followed. As a result, banks are using smart, AI virtual assistants to monitor transactions, keep an eye on customer behaviors, and audit and log information to various compliance and regulatory systems.
Big-data-enhanced fraud prevention has already made a significant impact on credit card processes, as noted above, and in areas such as loan underwriting, as discussed below. By looking at customer behaviors and patterns instead of specific rules, AI-based systems help banks practice proactive regulatory compliance while minimizing overall risk.
4. Improved loan and credit decisions:
Similarly, banks are using AI-based systems to help them make more informed, safer, and profitable loan and credit decisions. Currently, many banks are still too confined to the use of credit scores, credit history, customer references, and banking transactions to determine whether or not an individual or company is creditworthy.
However, as many will attest, these credit reporting systems are far from perfect and are often riddled with errors, missing real-world transaction history, and misclassifying creditors. In addition to using available data, AI-based loan decision systems and machine learning algorithms can look at behaviors and patterns to determine if a customer with a limited credit history might make a good credit customer or find customers whose patterns might increase the likelihood of default.
The biggest challenge with using AI-based systems for loan and credit decisions is they can suffer from bias-related issues like those made by their human counterparts, an issue discussed below under “AI risks and challenges.” This is due to how loan decision-making AI models are trained. Banks looking to use machine learning as part of real-world, in-production systems must try to root out bias and incorporate ethics training into their AI training processes to avoid these potential problems.
Emerging technologies are risky due to their immaturity and the limited time they have been in action. The risks of using AI are compounded by the fact that the field is evolving so quickly. In addition to the benefits of using AI in banking, companies must also consider the following risks and challenges:
AI bias: As noted, AI bias is one of the biggest risks in using AI in banking. This is due to how decision-making AI models are developed, namely by humans who bring their biases and assumptions to the training of the machine learning model. These biases can be magnified when the model is deployed, sometimes with troubling results. This definition of machine learning bias explains the different types of bias that can inadvertently affect algorithms and the steps companies need to take to eliminate them. Once a model is trained, it must be continuously updated to accommodate new factors (e.g., COVID-19) and head off “model drift.”
Explain ability and ethics: Financial institutions operate under regulations that require them to issue explanations for their credit-issuing decisions to potential customers. This makes it difficult to implement tools built around deep learning neural networks, which operate by teasing out subtle correlations between thousands of variables that are typically incomprehensible to the human brain.
Customer mistrust: In addition to complying with regulations, financial services companies must be mindful of customer trust when using AI tools. Chatbots prized for their convenience, for example, will cause customers to lose trust if they make mistakes.
Cost: Finally, the pace of AI innovation is both exciting and costly, Bennett noted. There is often a lag between the time an algorithm is created in the lab and when it is deployed, simply because it is too expensive to run it. But even widely adopted algorithms can prove too dear to use profitably.
“We have come across companies that have switched off certain algorithms because the benefit they gained from running them did not outweigh the cost of running them.
Article Written by: Dominic Vijay Kumar – Chief Technology Officer, ART Housing Finance (India) Ltd.
