Of late, driven by digital transformation, banks have started leveraging artificial intelligence (AI) & machine learning (ML) to analyze data in order to gain a deeper understanding of their customer’s behavior, primarily. This strategy further provides ample opportunities and significant benefits for financial companies—enhancing customer experience, preventing fraud, and mitigating risks to name a few.
According to the latest report, banks could be capable of reducing their workforce by 22% in the next few years. Also, by 2030, banks and financial institutions can save themselves a trillion dollars in technology costs, thanks to new AI models.
Fintech is already seeing the benefits of AI, including increased productivity and cost-benefit savings. With both insight and data analysis increasing in AI banks, the need for conventional banking services will dwindle.
Introducing Machine Learning (ML) in Finance Process
Machine learning algorithms can learn from various data and processes to find insights. Therefore, by extracting meaningful insights from raw data, this technology continues to provide accurate results. Further, doing this helps with solving complex problems in the banking & finance sector.
ML Use Cases in Banking & Finance
ML is useful to firms that operate in the banking and financial services industry. One reason ML is a good idea is that it cuts down on engineering-driven data modeling, which can be time-consuming and complex.
Let’s explore other ML advantages in banking.
What Are the Areas for AI/ML To Take Over in Banking & Finance
AI & ML both have major effects on banking, from automating processes to making complex finance processes easier. It is inevitable that AI/ML will shape how banks work, as clients have more money and are better served.
A few areas where these two emerging technologies take charge are:
- Risk Management
Machine learning algorithms process customer data to approve loans more conveniently and quickly. Before, when these algorithms were not as sophisticated, banks had to rely on a customer’s history to understand their creditworthiness. In this way, they faced difficulties approving loans.
Now, with the digital transformation and advancements in ML, the machine better analyses customers to process their loan applications more accurately, along with mitigating all the associated risks during such complex financial processes.
- Fraud Detection
Banks are prime targets for hackers and financial crimes. Integrating ML can help mitigate these risks by sensing any suspicious activity before it happens. Integration of AI & ML further allows banks to follow through with their pre-set protocols without violation, thus, reducing risk for the customer. The underlying benefit of this is that machines can perform high-level analysis in real-time that humans cannot do manually.
For instance, DataRobot provides an ML platform, exclusively designed to help fight fraud and illicit transactions in the Blockchain. By implementing algorithms that can detect abnormal or anomalous behavior, this software is well-suited to identify any theft and fraud in distributed ledger technology.
- Banking Customer-centric AI/ML Service
Conversational AI & ML in the form of chatbots, Robo-advisory, feedback, and many more, keep clients satisfied with a personal and tailored financial experience.
Virtual assistants aided by AI, including Cortana, Siri, and Alexa can now complete tasks for clients based on prescient research. Also, apart from completing clients’ tasks, these conversational AI can further assist them, making it easier to draw in with the bank.
RPA is being used in banking to automate tasks that would otherwise take up a lot of time, along with a high possibility to be error-prone. The advent of RPA ensured the reduction in the number of errors made; by successfully replacing humans’ involvement to perform such rule-based complex financial tasks.
- Credit Scoring & Churn Prediction
Old credit scoring systems use targeting. They don’t collect data on real clients, they aim to target those they might serve. However, modern credit scoring and churn prediction software can actually analyze real clients, rather than just sets of potential customers.
An AI/ML performs churn prediction for all potential new customers, on behalf of a bank. The predictive model is based on the customer’s preferences and will be used to personalize marketing messages. This allows the bank to increase its “customer lifetime value” by 45%, along with improving its marketing campaigns.
- Incorporating AI/ML With Cloud Computing
AI offers a comprehensive overhaul of the business. When used in conjunction with cloud computing, it improves an organization’s effectiveness, efficiency, and data-drivenness. This incorporation further helps evaluate accessible data, unlock priceless insights, and save money while enhancing company outcomes and predictive capabilities. Moreover, this enables companies to test out fresh concepts and raise the caliber of their products without incurring additional expenses.
- Algorithmic Trading
Banks are crafting targeted campaigns to enhance the experience for aging customers, along with making the banking process more convenient for them. Bank managers can now use AI to quickly correspond with customers on individualized, contextualized messages that offer more personalization. This use of technology saves both the customer and the bank time and money.
It was just a matter of time before AI and ML would infiltrate modern banking, and fundamentally alter the landscape, given the surge of fintech businesses and the quick evolution of technology usage. Using AI and ML will offer predictive data analysis, as banks and financial institutions predict customers’ actions in order to provide faster, more secure, and more personalized services to their customers.
Hundreds of data sources can be analyzed concurrently by machine learning algorithms, providing traders a distinct advantage over the market average.
Additional advantages of algorithmic trading include:
The Way Forward
As technology evolves, banking will utilize new layers of AI and ML to be more technically advanced. When choosing new technologies for your business, it is better to start with just one element and analyze it before incorporating any other.
Remember that AI and ML are complicated technologies that only improve with time. These advances are pivotal to any company that wants to stay ahead of the competition in the market.