Artificial Intelligence (AI) spend in the banking and finance industry in the United States has increased at 82.9% during 2018 to reach US$ 696.3 million. Over the forecast period (2019-2025), spend on AI is expected to record a CAGR of 28.4%, increasing from US$ 1,094.9 million in 2019 to reach US$ 6,289.1 million by 2025 – Report by ResearchAndMarkets.com
Machine Learning implementation in banks can work wonders, there is no magic behind it (well, maybe a Lil bit). Still, the after-effects of its application are stunning. Any successful machine learning model is skimmed through 3 crucial stages:
- Getting the right set of data – In banks, big data is prevalent and it’s mostly in the structured form. So, this should be easy to source.
- Building the right infrastructure – Having teams and technologies that can massage this data and give out critical insights is important to be built.
- Applying the right algorithms – Choosing form plethora of options like – Linear Regression, Logistic Regression, Decision Tree, Naive Bayes, kNN, etc. and applying the most suitable one in the model to derive results banks want is the final most critical step, after all, you need to have a highly accurate model, right?
If you are wondering where Machine Learning can be applied in banks and NBFC’s, then sit back and enjoy this extensive list:
- Portfolio Management and Trading using Machine Learning in Banks and NBFC’s
- Machine Learning to achieve operational efficiency and automation in BFSI
- Risk Management and achieving compliance in Banking and NBFC’s
- Recommendation Engines to enhance the customer experience in BFSI world
- Chatbots integration in Banks and NBFC’s
- Personalization and Customer Segmentation in banks to achieve higher sell-through
- To build a multichannel marketing strategy in the Banking and Finance Industry
- Determining credit scoring and smoothing loan management in BFSI
- Machine Learning for Fraud Detection in the Banking and Finance Industry
Now, that’s quite exhaustive! Believe me, this is just the tip of an iceberg that I have reflected here, there is a lot more to add to that. Artificial Intelligence is truly disrupting the way banks work and this is just the beginning of a revolution that will entirely change the “typical” working scenarios in BFSI. Strap on! We are now ready to zoom in and explore a new world of next-gen banking transformation.
Machine Learning applications in BFSI with their use cases.
Machine learning is a subset of artificial intelligence that allows machines to read and understand the data and derive valuable predictive insights form it. In this case, machines learn without being explicitly programmed!
Portfolio Management and Trading using Machine Learning in Banks and NBFC’s
“Be greedy when people are fearful and be fearful when people are greedy” – Warren Buffett
Whenever I think of trading, I think of this quote. Quite true, right?
Trading is a dare-devils game, uncertainty is high and risks are even higher, I wish we could foresee what will happen ahead and earn chunks of money. Well, this could have been just a dream a couple of years ago but today this is a reality! YES! Machine Learning has made this possible.
The market is driven by sentiments, there are numerous parameters to gauge and predict its movement. Banks and NBFC’s have big consumer data and analyst data available with them. They can implement an ML model to analyze that data and derive patterns. In real-time organizations will be able to make crucial decisions related to investments and execute that. Here is an example of that –
Tip: More parameters help you in getting more accurate results, the focus should always be on achieving accuracy and improving the model. The better machines learn the better you earn!
For portfolio construction and optimization, the development of investment and risk strategies, and predictive forecasting of long term price movements are some use cases suitable for the effective use of AI and machine learning.
Use Case:
Learn how Tata Mutual Fund uses ML to create intelligent investment portfolios
The machine learning model then identifies patterns in the current and historic data and identifies the portfolio that delivers maximum returns, based on rules and parameters assigned. Once that is extracted, the model then moves to predictive the trend in the next 30 days and once the time lapses, the model then learns from its prediction and automatically makes logical changes and again predicts the next 30 days.
The cycle repeats and once the model has achieved the desired accuracy it moves on to predict the next tenure.
Utpal Sarma, Head – Business Analytics, Tata Asset Management says that the model was tested for 107-108 months. “As of date, my processes are quite efficient and take only 3 hours to look at data and relearn,”
I am sure banks would want to make the best use of Big Data that’s available with them and leverage the power of Machine Learning in Portfolio Management and Trading.
Chatbots integration in Banks and NBFC’s
Bankers are getting smart by integrating intelligent chatbots to solve queries faster and also be able to reduce costs drastically! Quick transactional support is the ask of consumers now as everything is digital and the turn-around-time to solve any query needs to be as low as possible.
Simple tasks such as balance inquiry, bank account details, credit card summary, loan inquiries, etc. can be handled by a bot, quite easily!
According to a report released by Juniper, chatbots will be responsible for over $8 billion annual cost savings by 2022. According to Gartner, by 2020 chatbots will be handling no less than 85% of all customer service interactions.
It has incredible usability like:
- 24/7 Support – Digital transformation has enabled users to transact around the clock and with that arises a requirement to foster 24/7 support, the human cost to that will be way too high but integrating a chatbot feature will not only automate the process but also eliminate the need of humans to solve mundane problems.
- New-Gen Bank – Every Millenial desires to have an account in the bank that is hi-tech and complements their need to have seamless banking. The more you adopt technology, the higher the chances are for you to attract prospects and convert them into customers.
- Conversational Interface – Phone calls are patience-testing, there are so many to and fro processes that a customer finds it difficult to cope up with. Studies have already shown that people find phone calls tedious and slow as compared to instant chat. So, integrating chatbot is a brilliant mix of conversation and speed.
Banks that have adopted chatbot feature:
- Bank of America – They have integrated a chatbot called Erica, apart form solving queries this chatbot also suggests ways to save money and send reminders and notification at appropriate times.
- Wells Fargo – Their chatbot assistant uses Facebook Messenger to respond to natural language messages from users, such as how much money they have in their accounts, and where the nearest bank ATM is, etc.
- HSBC (Hong Kong) – Amy is a customer servicing platform that takes the form of a Virtual Assistant Chatbot for corporate banking at HSBC Hong Kong.
- SEB (Sweden) – SEB has released Aida, a female chatbot for customers — and a follow up to an internal chatbot SEB released called Amelia, for employees. In Amelia’s first three weeks, over 4,000 conversations were held with 700 employees, and she solved the majority of issues without delay.
Personalization and Customer Segmentation in banks to achieve higher sell-through
The success and failure of any marketing campaign depend on how well the audience is segmented. Banks look at customer segmentation to gain insight, on how to decide on specific offers, improve customer service, and understand customer behavior & more.
Based on innumerable parameters, banks can unleash the potential of artificial intelligence and achieve maximum sales.
Customers’ expectations today are skyrocketing! Based on their preferences and uniqueness it is very important for banks now to carefully monitor their nitty-gritty and offer them products and services that suit their needs. Today, banks need to know the customer more than ever.
With immense terabytes of data that are available in a banks’ storage, it is important to extract insights to obtain quality customer experience. Data ingestion does take place at banks. Banks sometimes fail to capture real-time data of customers at multiple check-points, this incapability results in inefficient selling, therefore any recommendations made to customers do not see the light of the day as desired.
How do banks achieve accurate recommendations and segmentation?
The customer behavioral pattern is an essential data point to determine the customer’s taste and preferences. Banks have enormous data extracts that can be utilized for this purpose.
AI-based machine learning models can easily segment customers using a much wider variety of data including browsing behavior, prior purchases, demographics, household data from third parties and more.
In terms of Machine Learning, there are two most popular algorithms for customer segmentation are k-means and hierarchical clustering.
No-coding way to achieve accurate segmentation using AI and ML!
Driverless AI is the in-thing, a seamless AI model that can simply take in data and churn out a classic categorized segmentation for your products. H2O Driverless AI is an award-winning platform for automatic machine learning that empowers data science and technical marketers to scale machine learning efforts by dramatically increasing the speed to develop highly accurate predictive models.
Here is a PayPal case study. Unfortunately, Paypal could not see that things had gone bad until it was too late. To get ahead of customer churn, the data science team needed to develop models using a variety of data that would predict customer churn and allow the company to quickly take action to re-engage the customer – Discover how PayPal resolved this by using H2Oai’s driverless AI Read more.
How can ACT21 Software help?
As a boutique BFSI solutions innovator, ACT21 Software is a front runner in providing consultancy for banks and financial institutions to integrate Machine Learning techniques into their operations and derive exceptionally next-gen results.
We believe, every bank needs to adapt AI to be able to attract and retain next-gen bankers, who expect quick TAT at every touch-point. With our offerings you would not need to establish a team of data science experts, rather just source your data points and put them in an ML machine and let that machine do all the modeling for you and give you just the output you need, business have lean time to make decisions and our offering gives them a picture-perfect dashboard that enables them to take critical business decisions
Want to know how you can leverage this incredible technology? Connect with us today!
Concluding Note
I am smitten by ways in which AI and ML can possibly revolutionize the banking arena, customers and banks are in a win-win situation with this change. Banks can drastically reduce costs and customers can gain more by paying less.
I am looking forward to establishing incredible relationships while walking the futuristic path.