As we step into the new customer experience, the demand for fast and high quality financial services has increased exponentially. There are several successful cases which confirm the benefits of the use of Artificial Intelligence and Machine Learning in finance.Â
In this piece, we will discover the role of Machine Learning in the finance sector with the help of case studies, so as to have a clear insight into how Machine Learning is helping optimize the work of Fintech companies.Â
Role of Machine Learning in Finance
Fintechs are actively making investments in developing new technologies to improve the quality of work with their clients, so as to receive the best financial services.
According to the data provided by Mediant Inc., the statistics clearly indicate, the next 5 years will see artificial intelligence and deep learning in finance become the leading investments in the finance sector.
The primary function of machine learning algorithms is to identify work patterns and correlations among the huge amount of information available. Machine Learning, has therefore become almost omnipresent especially for the functions of optimization, ensuring security of data, optimizing portfolio and doing much more.
Applications of Machine Learning in Finance
The contribution of Machine Learning in the finance sector, although not very apparent, is nevertheless, majorly responsible for most of the advancements we see today in the finance sector. And here are some examples, as to how.
1. Customer experience:
Machine Learning helps organizations improve the customer experience, services, optimizing costs etc. It has also introduced the concept of automation which helps in saving the human efforts and makes their realization more productive.
2. Onboarding customers:
The onboarding process a customer goes through is fundamentally responsible for determining the customer’s current relationship with the organization. The use of Machine Learning has made the onboarding process for customers quite simple and hassle-free, and it has also made it a matter of just a few minutes.
3. Prevention & Detection of frauds:
Machine Learning algorithms come in handy when it comes to detecting frauds in the financial sector. We can see many banking organizations using it to monitor a significant amount of transactional parameters at once, for all the accounts on a real time basis.Â
4. Asset management:
With the help of Machine learning and AI, asset and wealth management firms are able to improve their investment decisions. Around 13.5% of the AI vendors in the banking sector exist for wealth and asset management.Â
5. Stock market and trading:
Machine Learning also helps in monitoring the latest updates of market information and trade results in real time. The algorithms help in detecting patterns that can have an impact on the stock prices.
This is not it! There are various other applications of Machine Learning in the finance sector like underwriting and credit scoring, portfolio management etc.
Following are some case studies which will further provide an insight into how Machine Learning is gaining importance and how that has led to the rise of dedicated startups which are doing very well in the market.
Case Studies About Machine Learning in Finance
Mentioned below are some incredible examples of organizations which have utilized Machine Learning to help all the parties involved in the financial sector.
1. Signifyd
A very popular machine learning startup in the banking industry, its main task is to identify fraud at the checkout stage. With the help of deep learning algorithms, this solution works on verifying the identity of the customers by going through the previous transactions and user behavior patterns.
2. Appzen
This is another successful machine learning startup which works for an automated back-office audit. It helps an individual analyze contracts and invoices to check if there are any mistakes, frauds or any changes in the spending patterns.
3. Zest
This Machine Learning startup aims to improve the process credit scoring. This solution uses advanced credit modeling to identify possible risks, thus helping the lenders make more informed decisions.Â
4. Cleo
Cleo is a smart budgeting application which analyzes the spending patterns and habits of people, gives budgeting tips, suggests good financial tips and helps the users to achieve them.Â
FAQs About Machine Learning
Q1. What are the main use cases of Machine Learning in the finance sector?Â
Ans. Mostly, use of Machine Learning helps finance startups to have access to opportunities to analyze the data of customers, understand their behavior patterns, monitor their spending habits, improve customer experience, prevent frauds etc.
Q2. Will AI completely replace humans in the finance sector?Â
Ans. Artificial intelligence will definitely replace humans in many tasks but we cannot expect that it will completely replace humans because there are some tasks which are better done by humans. Artificial Intelligence can be used to automate less important tasks in which human intervention is not necessarily needed.
Q3. Will Machine Learning and AI reduce job opportunities in the finance sector?Â
Ans. AI and Machine Learning have definitely eliminated the need of humans in many tasks but there are still many functions in which human intelligence is needed. While AI and Machine Learning have closed the doors of some opportunities for humans, they have simultaneously created some new opportunities as well, where human skills can be utilized.
Finally
The contribution of Machine Learning is not as apparent as it should be but at the back end, Machine Learning continues to play a very fundamental role in introducing productive advancements in the finance sector and shall continue to do so with more effect in the future.
