Banks say “Hello” to machine learning

Smooth ATM cash withdrawal after implementing ML

A leading consulting firm conducted a survey to assess customer satisfaction across five core areas of the customer–bank relationship such as ‘knowledge of customer’, ‘trust and confidence’. Less than half (37%) customers believed that the bank understood their needs and preferences.

Banks need to make an effort to understand their customers better in order to serve them better. Most banks believe that Artificial Intelligence, Big Data and Machine learning (ML) are helpful in understanding and serving their customers. But few think that they have the resources and ability to implement machine algorithms.

The purpose of this article is to discuss the possible outcomes of Big data analysis using machine learning in the banking industry. Initially, we describe ML and its application in banks. Then we list the challenges of improvement in the banking sector. Finally, we hash out the picture of a successful data-driven bank. 

Artificial intelligence solutions will lead to $34- $43 billion in annual cost savings

As the name suggests, machine learning is a technology where the machine (computers and other web devices) can learn from the data it receives. It can analyze the data and accordingly modulate the output. Often times such systems are referred to as expert systems, thinking computer systems or even artificial intelligence.

ML technology is usually employed for two purposes: a) pattern recognition or recognizing the failure of a behavior pattern and b) predictive abilities. For instance, if a customer’s credit card use pattern is known then any deviations from it can be detected as fraud. If a credit card user is continually falling behind on payments then it can be predicted that, if disbursed, they will default a big loan. These machine learning algorithms can be applied by several approaches such as neural networks, deep learning, or clustering.

The application of ML technology in banking has advanced by leaps and bounds in the past five years. This trend has been studied carefully by marketing research firms. One report notes that by 2020, implementing artificial intelligence solutions, including machine learning, will lead to $34- $43 billion in annual cost savings and new revenue opportunities for banks and financial institutions.

In many banks, crucial business processes of the bank such as credit scoring, liquidity controls, client analysis and employee analysis are performed using ML. Furthermore, machine intelligence is applied to predictive services such as loan default prediction, chatBots, ATM cash demand prediction, and fraud detection.

Process of implementation of Artificial Intelligence in Banking

Implementing machine learning in banks poses some challenge. At present, only 32% financial institutions use some form of artificial intelligence. Some banks that do not use machine learning as they perceive it as ‘new, untested and risky’. Banks view ML like a “black box”: it gives predictions or recommendations but it is almost impossible to understand underlying algorithms. Technology companies that offer ML services strive hard to change these perceptions. Some banks have concerns around privacy of their customers. The technology is continually evolving to address these concerns. Now the leadership, including regulatory authorities also, needs to change traditional approach and embrace technological advances such as machine learning. 

In order to develop machine learning capabilities, banks seek help from technology companies such as BitRefine. The top management is wary of predictions that ML model provides. These leaders often question the basis of such predictions and recommendations. The specialists at the technology companies such as BitRefine offer services that include building predictive models and help top management to accept those models by generating proper interpretations and visualizations. ML companies provide services that are tailored for a particular bank. For instance, smaller credit unions may primarily need loan default prediction, while larger banks are more interested in chatBots. Initial investments are low and the return on investment is almost immediate in few weeks.   

Banking industry reveals valuable insights using big data and machine learning

A data-driven bank owns big volumes of raw data from various resources as customer accounts from other financial institutions (“wallets”), behaviors observed from social media, and credit monitoring agencies. A data driven bank adopts technology required to analyze this data, and top level executives make a decision based on the analysis. Overall, analyzing big data using ML reveals valuable insights quickly. This enables banks to reduce risks, reduce costs, and improve internal processes.

A successful, fully data-driven bank is able to connect with its customers effectively. It develops and sells financial products based on customer needs and preferences. A data driven bank monitors the efficiency of its business processes by employing machine learning. Finally, perhaps the most important attribute of a data driven bank, it accurately measures its success by how swiftly and how well it was able to serve its customer.

This vision of a successful fully data driven bank leads banking sector to take the crucial next action steps. The time has come when banks say “Hello” to machine learning.

Diagram - implementing Machine Learning in Banking

September 10, 2017

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