Artificial intelligence in banking is becoming increasingly prevalent in our daily lives, and banks must keep up with this trend by implementing it widely. To be successful, a company must undergo a fundamental change that affects all levels of the business.
Technologies based on artificial intelligence (AI) have grown further, and their revolutionary effects are becoming more obvious across industries. AI-powered machines are developing clothes lines for retailers, recommending digital content based on personal interests and tastes, and even starting to outperform skilled medical professionals at spotting cancerous lesions.
According to McKinsey, using artificial intelligence in banking could generate up to $1 trillion in value annually.
But many banks have found it difficult to expand the usage of AI technology throughout the firm after testing them out in a few specific use cases. The lack of a clear AI strategy, a rigid and underfunded technical core, fragmented data assets, and antiquated operating methods that impede communication between business and technology teams are a few causes.
In addition, the COVID-19 epidemic has pushed several digital interaction trends, and big-tech firms are eyeing financial services as the next adjacency. Incumbent banks must utilize artificial intelligence in banking as the basis for novel value propositions and standout client experiences to compete successfully and flourish.
Why Must Banks Become AI-First?
Over the course of several decades, banks have incorporated the most recent technological breakthroughs to change the way customers interact with them. In the 1960s, banks began offering automated teller machines (ATMs), and in the 1970s, they began accepting electronic payments using credit cards.
The widespread use of 24/7 online banking in the 2000s was followed by mobile banking in the 2010s.
Artificial Intelligence (AI) in banking has become a reality in the digital era because of low-cost storage for data as well as increased connectivity for everyone, and quick advancements in AI technologies.
As a result, these technologies can lead to greater automation that often improves human decision-making in speed and accuracy when adopted after adjusting for risks. Artificial Intelligence (AI) in banking has the potential to generate $1 trillion in added value for banks each year.
Ways In Which AI And Ml Will Shape The Banking And Finance Sector
The future of finance will heavily rely on artificial intelligence in banking and machine learning in banking. They will support banks in data analysis, client behavior forecasting, and financial service customization.
How financial services are run will fundamentally change in favor of data-driven banking. Many banking data analytics procedures will change during the next five years thanks to advances in machine learning and artificial intelligence through the following ways.
Conversational Chatbots & Virtual Assistants
Customers demand a more conversational experience when dealing with their financial institutions. Their expectations are the same as those of companies like Amazon, Netflix, and Uber when it comes to customer service. It’s now conceivable in banking, thanks to chatbots and virtual assistants.
Customers have learned to demand better service from other businesses, and banks are no exception. Intelligent chatbots and virtual assistants may answer client questions about their bank accounts and other financial transactions at any time or night. Customers can also use natural language to send money.
Customer Sentiment Analysis
Customer sentiment analysis is a significant area for banks to deploy Artificial intelligence in banking in the age of big data and machine learning. There is a wealth of information about their customers that banks currently have, but much of it is unstructured and hence difficult for computers to grasp. On the other hand, the data itself may be understood and analyzed by artificial intelligence (AI).
Social media and customer comments can also be analyzed using these methods. Banks can use AI to forecast how consumers will react to certain events, such as the launch of a new product or service, or even changes in market circumstances that could affect them by analyzing what people say about their bank online.
ML-Driven Underwriting Processes
When determining a customer’s creditworthiness, it might be frustrating if they have no prior credit history with the bank. Big data and machine learning (ML) examine more than 10,000 data points to determine a person’s credit worthiness. As a result, a wide range of consumers, including students and those who are self-employed, can be pre-approved for a loan.
Even for corporate lending, AI-based underwriting will be able to streamline this complicated process, examine market patterns, pinpoint loan risks, predict future behavior, determine the likelihood of fraud, etc.
Personalized Banking Data Analytics
Personalization is the most significant advantage of AI and machine learning in banking. A bank of the future should be able to deliver personalized financial advice, alerts, and suggestions based on an individual’s specific financial needs and goals.
Artificial intelligence in banking is still in its infancy as a technology in the banking industry. Banks is already using it to identify and predict the financial needs of their consumers.
To provide personalized financial services, banks must first learn how their consumers prefer to be addressed and communicated. Lots of work is required in this area. Banks need a lot of data, but it’s spread over many systems and departments, making it difficult to access and analyze.
In order for this information to be mined and sent to a consumer, the help of Artificial Intelligence is needed here.
Use Cases for AI & ML in Banking
Intelligent Automation
In the future of data analytics, many manual processes will be automated with the help of intelligent process automation thanks to ML technologies. Automation of paperwork, chatbots, and employee training gamification are examples of prospective process automation in the banking data analytics industry. As well as cutting expenses, it improves the client experience and allows banks to expand their operations.
Integrated Command & Control Systems
It is becoming increasingly difficult for banks and other financial institutions to manage their data as well as that of third parties in today’s financial services world. This produces data silos that are not integrated into a single source of truth system. In the absence of the ability to properly examine all of their data, banks cannot make informed judgments.
Various data integration and machine learning in banking algorithms can be employed to produce these “single source of truth” systems. This is referred to as an Integrated Command and Control Center (ICCC). With this method, banks may make better judgments based on all of their accessible data rather than just a part of it.
Cybersecurity and Anti-Money Laundering
In the near future, banking will become safer thanks to the innovative use of AI and machine learning in banking. The financial services industry will benefit from AI and ML by enhancing its cybersecurity and anti-money laundering procedures.
Several banks are using AI and ML to detect suspicious or anomalous transactions that could be linked with money laundering. In the following years, the use of AI and ML technologies is likely to rise dramatically.
A more efficient service that saves time, money, and resources will result from integrating these technologies. Further strengthening the security systems of banks, enterprises, and individuals to prevent cyber assaults will also help.
AI-Powered Fraud Detection
ML systems can identify fraudulent activity by analyzing millions of data points. Improves real-time approval quality and reduces the number of incorrect rejections. It will not wait until after a crime has been committed to discovering any unusual activity from specific bank accounts.
At least $2.92 is spent by financial institutions to recover every dollar they lose to fraud. This is where AI and ML can majorly impact the banking industry. Even though banks employ monitoring systems, they are typically based on data from prior transactions.
Machine learning-powered algorithms can readily distinguish between fraudulent and legitimate credit card transactions because they are built on a massive database of transaction data.
Conclusion:
There is hope that AI and ML will be adopted more widely by banks in the future, despite their infancy. Heavily needed technologies like artificial intelligence in banking and machine learning (ML) in banking are finally starting to dawn on financial institutions.
Truth be told, most banks have rigid procedures in place that make it difficult to transform into a technologically-driven business in both the operational and organizational senses. In order to overcome this, banks must have faith in these technologies and be prepared to use them.
Ascend Analytics Consulting Services can be useful if you want to scale banking through artificial intelligence (AI). You can make use of the full potential of your financial and cosumer data with the assistance of our thorough data analysis and financial consultancy services.
Our team of skilled analysts will collaborate with you to pinpoint important trends and chances, then create a special strategy to support your objectives.