The modernization of the digital world and vast technological advancements have shifted our lifestyle in many different ways. AI technologies are increasingly integral to the world we live in and become even more influential in financial services businesses’ success.
Over several decades, tech giants and top banks have prioritized technological advancement with investments in AI applications to service their customers better, improve performance, and increase revenue.
Why Must Banks Lead The Shift to AI-First?
Undoubtedly, AI is changing the face of the banking industry worldwide and plays a vital role as a significant disruptor of today’s banking and financial sector. The AI technology empowers banks and financial institutions to redesign their operation and services to cater to the current market needs. Most importantly, bank and financial institutions can transform their customer experience interventions and improve customer acquisition.
McKinsey estimates that AI technologies could deliver up to $1 trillion of additional value each year for global banking. In the same report, they also highlighted that there would be chances for banks to be overtaken by competition and losing customers if they failed to transform their core strategy with the intervention of AI technologies. Let’s have a look at the possible risks by the following four current trends.
Big Techs have changed their core business models and aggressively entered financial services.
Several leading big techs have already adopted this advanced AI technology to overcome traditional banking challenges and push deeper into financial services. In 2019, Google had to expand its plans to become a digital banking services provider in the U.S. Meanwhile, Apple already tapped into consumer demand for digital banking with the launch of its co-branded Apple Card credit card with Goldman Sachs. Besides, leading technology giants also have built extraordinary market advantages and already gained a foothold in financial services in specific domains, especially in payments, lending, and insurance.
Customers have a greater expectation of digital banking offerings.
According to Deloitte, 35% of customers have increased their online banking usage during COVID-19, and this pandemic has accelerated the importance of digitalization. On the other hand, today’s tech-savvy customers demand banking services that provide a more personalized banking experience and benefits tailored to their financial goals and personal needs. They see financial services as an enabler for other aspects of their lives. They expect their banks to deliver the products and advice they need in an efficient, timely, and contextually relevant way.
The rapid adoption of advanced AI technologies by leading financial.
Nearly 60% of the financial-services sector responded that their companies had embedded at least one AI capability in McKinsey’s survey. In other words, banks are extending the use of AI technologies to improve customer experiences and back-office processes.
Digital ecosystems are driving digital transformation.
The digital ecosystem enables a seamless and integrated experience across multiple devices to connect with today’s hyper-connected consumer. For example, in China, WeChat users can chat, send a message, transfer money, order food, book a grab, and play games in the same app. Besides providing a better customer experience, nonbanking businesses expand their business models and embed financial services and products in their journeys. As a result, banks have to rethink how to participate in digital ecosystems and leverage AI to benefit from the full power of data available from these new sources.
To maintain a sharp competitive edge, every bank and financial institution has to embrace AI’s application in their daily operations.
So, how does AI changing the face of banking?
Personalized Banking and Enhanced Customer Experience
Artificial intelligence (AI) develops a better understanding of customers and their behavior by analyzing past transaction patterns and data to discover hidden insights and provide a useful recommendation. Banks believe that AI’s most valuable use is improving the customer experience, such as digital assistants and voice-assisted channels, personalization of the customer journey, and digital marketing. The AI technology allows banks to build strong relationships with the customers, improve customer satisfaction, unearth hidden from vast troves of data, and deliver meaningful customer engagement by adding intuitive interactions and personalized features.
Smart Financial Banking
Today, most banks adopt the typical Personal Financial Management (PFM) approach by offering basic statistical analysis on customer’s spending patterns and goals tracking. The AI-powered Smart Financial Management will bring the AI into action to learn the customer’s financial transaction patterns.
This ability helps to spur financial inclusion among the underserved market where the app is now intelligent to provide actionable financial recommendations base on customer’s cash flow and preference. The apps can provide financial guidance and education to help customers achieving financial health in a shorter time. The app will forecast the future cash flow and hence respond with the right recommendation at the right time. For example, a pre-approval loan will help when it senses that the customer needs cash.
The intelligence enables the apps to recognize and brings the needed services closer to the customer base on their personal usage pattern. Imagine that an app will be able to detect and help to avoid the potential of redundant bills payment which has been paid recently.
The customer demands for the intelligent assistant is increasing with the maturity of the technology. Ultimately, the app should be able to provide a guided approach towards effective financial management without the customer noticing it.
Enhanced Credit Decision
Examining an individual’s creditworthiness is a crucial feature of any bank or financial institution. ML formulas are useful to convert customer data right into a credit rating, which banks or lending institutions can use. AI-based credit scoring automates the credit scoring analysis and assesses potential borrowers’ creditworthiness, and reduces the rate of loan defaults using predictive analytics based on historical data. It allows banks to make smarter underwriting decisions by utilizing various factors that more accurately assess traditionally underserved borrowers, like millennials, in the credit decision-making process.
Intelligence Search and Document Analysis
Natural Language Processing (NLP) enables banks to automate specific document processing, analysis, and customer service activities. With AI and NLP technologies, we able to extract, compare, match and transform complex regulations into machine-readable rules and reduce operational risks associated with meeting compliance and reporting to help banks shorten the guideline review process from a few weeks to mere seconds.
Fraud and Risk Management
Undoubtedly, the online threat is a massive concern for banks as they digitize on a grand scale. Hence, the bank needs a reliable system to detect fraud and immediately take necessary action to handle the situation in an organized manner. Therefore, AI is the game-changer for fraud and risk management. With the capability of AI, we able to reduce false-positive cases significantly, lower operating costs related to risk and fraud management, and improve the ability to detect fraud in real-time. AI-based machine learning can play a vital role in the banking sector. By implementing an automated fraud detection program and false positive reduction module, banking organizations can boost anti-money laundering and credit card fraud detection accuracy.
Predictive and Prescriptive Analytics for Future Outcomes and Trends
Artificial Intelligence can help banks forecast financial market trends and recommend an optimal action plan with its capability to predict future outcomes by examining past historical data and behaviors. For example, an AI-powered predictive and prescriptive loan sales performance system can forecast future loan sales to make timely decisions to maximize loan performance and recommend optimized approaches to achieve the sales performance targets. Besides this, with its power of Machine Learning, an AI-based system can identify fraud, detect anti-money laundering and anomaly data patterns, and alert staff. Moreover, AI can investigate the past to predict data points’ future behavior, helping the banks predict borrower default risk probability and up-sell and cross-sell successfully.
AI-based Chatbots
AI-based Chatbots are known for identifying the underlying intent behind the text and emotions in the chat and react to it most appropriately. Harnessing the capability of Natural Language Processing (NLP) enables the business to has the ability to provide the relevant answer and offer proactive service when it detects a potential problem. They can act as customer service representatives and serve customers consistently 24×7 at anyplace and anywhere. Digital personal assistants or virtual assistants powered by NLP can improve their ability to provide appropriate responses and solutions from past conversations, allow banks to save time, improve efficiency, and save millions of dollars.
The Future of AI in Banking
INFOPRO has taken the leads to cultivate the adoption of AI-Driven practices in the banking industry. With the award-winning AI-Driven Digital Banking Platform, the banking solution comes with the AI-as-a-Service engines to service the AI-powered services across the front, middle, and back office. The readily available AI use cases are fully integrated with the entire banking ecosystem and can be easily consumed by external systems via API.
The platform is designed for the deployment of new AI use cases through its service layer, and hence it is agnostic to 3rd party systems adopted by the banks.
INFOPRO offered a comprehensive end-to-end, complete suite AI-driven digital banking solution to achieve higher profits, at-scale personalization, distinctive omnichannel experiences, and rapid innovation cycles.
By embedding AI and Machine Learning into our products, we have accelerated the release of explainable models such as supervised learning, unsupervised learning, deep learning, Natural Language Processing (NLP) that underpin new AI use cases that focus on creating seamless customer journeys and automating manual banking processes with self-learning capabilities.