Artificial intelligence and Data Science technologies are adopted by an increasing number of industries. In many, their transformative impact is increasingly evident in saving time and money. The banking industry needs to deploy these technologies at scale to generate accurate results which in turn generate better customer experience, more sales, and higher profits. A holistic transformation of banks will require multiple layers of the organization integrating with the technology to produce the desired results. For global banking, it is estimated that AI technologies could potentially deliver up to $1 trillion of additional value each year.

Also Read:- 4 Ways That AI Is Improving the Customer Experience

The banking system is complicated and needs monitoring and an error-free process to ensure its success. Now imagine having the ability to see everything, everywhere. All the time. Every interaction with customers. Every moving part and every financial transaction, anywhere in the world. This is what the application of AI and Data Science promises. Currently, these are being applied in a variety of banking needs including communications, customer support, recruiting, and asset management thereby drastically improving its process and accuracy.

In this blog, we propose to help articulate a clear vision for the impact of AI & Data Science in the banking industry. We cover:

1. Why adopting an AI-first approach is essential to banking?
2. How can Data Science benefit the banking industry?
3. What are the obstacles in deploying these technologies at scale?
4. Some use cases of AI and Data Science in Banking.

1. Why adopting an AI-first approach is essential to banking?

The banking industry has constantly evolved in past decades, and the infusing of AI-powered digital technology has redefined how customers interact with them. Essentially, technology has become more accessible due to the relatively cheap cost of data storage and processing, advanced connectivity and access, and the progress made in newer fields. AI leads to higher automation while improving the decision-making abilities and value generation for the banks.
Banks can utilize AI in many areas such as Marketing and Sales, Risk, HR, Finance, IT, and other operations. This will not only help in boosting revenues through the personalization of services but also lower costs with automation, reduced errors, and efficient utilization of resources. AI will also open doors to new opportunities for the banking sector with accurate insights derived from large sets of data.

The banks that embrace AI to play a central part in their future strategies are bound to experience better performance and customer loyalty going forward. This disruptive nature of AI can drastically improve the ability the gain higher profit, personalize at scale, create distinct user experiences, and speed up innovation cycles. For instance, AI is used by nonbanking businesses apps such as Paytm in India is embedding financial services and products which deliver compelling experiences for customers while disrupting traditional ways of banking and services.

2. How can Data Science benefit the banking industry?

In the past decade, banks have widely adopted the use of Data Science to identify customer predictions, fraud detections, and risks while ensuring better customer growth. Data science can solve some of the major challenges faced by banks today, such as:

Proactive monitor of customer experience: With the help of analytics and insights into customer transactions and trading activities, banks can understand their client in-depth while delivering the best services. Proactive Customer experience (Cx) monitoring will result in higher levels of engagement and retention.

Customer support operations planning: The banks can analyze data to plan operations effectively hence reducing operation cost. For example, forecasting Call/chat volume frequently, IVR optimization, directing traffic to unassisted channels, deploying intelligent chatbots, etc. Further, it helps in better customer support by utilizing data to analyze customer queries, feedback, and sentiments to understand needs & issues proactively.

Effective personalized targeting: Identify relevant cross-sell offers and design personalized recommendations for customers based on life stage and affluence. Banks can also generate more business opportunities by collecting data from customers and creating new business models, provide additional services and find new sources of income with the help of insights derived from the collected data.

Risk Assessment of clients in real-time: Data Science helps banks enhance risk assessment where relevant insights can be extracted in greater detail for assessing the risk profiles of the credit applicants and devise better collections planning through delinquency-based strategy focusing on high-risk customers. This will help the early-warning systems to predict in real-time how client’s loans are going to be repaid and to foresee a defaulter based on their history and credit report. Data science helps streamline accurate risk modeling in investment banking, where the risk-reward ratios are calculated for investments with risks. All these features will help banks to lower their risk costs, and to become aware of fraud more quickly.

Enhanced employee productivity: Banking will see significant improvement in its employee productivity and decision-making ability with the help of advanced employee engagement analytics. This will provide faster and more accurate responses to regulatory requests and streamline better decisions for everyday activities.

Digital marketing effectiveness: Digital efforts of the banks will reap more favourable resulting in more lead scoring to drive customer acquisition. It also gives real-time insight into the GA reporting and dashboarding that are crucial for understanding the marketing effectiveness.

3. What are the obstacles in deploying these technologies at scale?

Traditionally, banks have a robust technology system in place that delivers stability especially in supporting payments and lending transactions. However, banks need to update their legacy systems to support full-scale AI and Data Science adoption. Most banking systems lack the capacity and flexibility to support different requirements such as data processing, real-time analysis which most of the AI-focused applications require. Another factor is the fragmented storage of data by separate business and technology units resulting in data analytics teams not realizing the full picture.

Banks are more traditional when it comes to the active deployment of AI and Data Science in their operations. By adopting a weak core technology and a lack of centralized data bank bone, it is harder to generate relevant usable insights in real-time. Ideally, they will need a set of tools and processes to scale Data models and deploy AI in scale. Adopting this will help streamline and standardize processes to build, test, deploy, and monitor models, in an accurate way.

4. Some use cases of AI and Data Science in Banking are:

1. The use of AI-powered intelligent Chatbots to provide more consistent customer support while increasing sales and saving on costs: It is one of the widely proposed use cases of AI and helps in assisting the customers with their doubts, feedback, and complaints. By providing a personalized experience, these bots can be deployed on apps, websites, and other customer interaction checkpoints.
2. Proactive customer experience monitoring and alerting system – Identifying the issues/pain points that the customers are facing with the help of near real-time analytics. Analyzing data that is produced from customer transactions, feedbacks, legislative initiatives, and thousands of other factors using Data Science and proving alerts at the rights time to take corrective actions.
3. Next best action recommendation – A unified cross-product can allow the banks to create new cross-channel personalization solutions that will become a source of income and engagement in the dynamic marketplace. Furthermore, there can be more data backed accuracy on decisions such as whom to target, what to target, how (channel) to target, when to target based on Machine learning.
4. Introducing seamless automation to boost productivity and presence across banks – Traditional manual intensive banking activities e.g., Underwriting process can be substantially decreased with AI automation. This can result in reduced workload for banks and their employees, 24×7 service, and a high degree of customized services.
5. Adopting Data science in Risk analysis management to explore, analyze, and mitigate risk from various perspectives in real-time – Risk management is vital to the safety, reliability, and profitability of the bank daily. Data science forms a critical part of the operations that help in reducing risk by identifying, prioritizing, and monitoring them and reduce potential monetary losses.
6. Data science-driven anomaly detection finds suspect behaviour to prevent fraud -The detection and suppression of fraudulent activities related to bank accounts, cards, transactions, and other irregularities are tackled by Machine Learning algorithms. For instance, if a customer does an unfamiliar transaction from a new device, the system may ask for additional security questions to ascertain identity.

Also Read:- Reimagining HR with Chatbots and Analytics

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