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Use of Machine Learning and Artificial Intelligence In Finance

4 min read
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In today’s digitally emerging world, everybody wants to be at the top of digital transformation, and the fintech industry is no exception. Since digitalization has become necessary for businesses to accomplish growth, tech companies are going the extra mile to blend next-Gen technology with today’s tools to start automation and bring a significant change. While this change is projected to streamline the operations, advanced technologies like Artificial Intelligence and Machine Learning are expected to bring a remarkable change in how the finance and accounting industry functions.

In this post, we will talk about the possible usage of machine learning and artificial intelligence in Financial technologies.

How Do AI and ML Strengthen The Finance Industry?

In 2015, IBM conducted a study saying that the finance and insurance industry represents approx 7.4 percent ($1.5 Trillion) of the U.S. GDP. The financial industry, in particular, collects tons of data every day related to customer queries, company liabilities, and most of this data is either confidential or restricted. With the increased risk of cyberattacks, it has become crucial to have a robust data safety system.

Both machine learning and artificial intelligence worked together to relieve the safety-related concerns in the finance industry. Talking about how these technologies help to streamline the operations, ML and AI work by extracting meaningful insights from raw data sets to provide accurate predictions. These predictions might be related to any fraud, possible loophole in the functionality, or anything else that might pose a severe threat to the integrity in the future.

Leading fintech companies have already started incorporating these two technologies to improve the finance industry by identifying and resolving ongoing and forthcoming alerts. These are the top 10 fintech companies in America that are on a mission to transform the finance industry with high-end technologies.

Here are the possible machine learning use cases in the finance and insurance sector.

1. Financial Monitoring

Machine Learning can be used to improve the algorithms to enhance security significantly. Data scientists have been working on this technology for years to detect alerts such as money laundering or other online frauds.

Machine learning blended with artificial intelligence is expected to hold the most advanced cybersecurity networks in the coming days.

2. Make Investment Predictions

One of the most exciting usages of machine learning is that it makes highly effective marketing predictions based on the previous fluctuations. Machine learning and artificial intelligence work as algorithms to track market changes and ups & downs to develop the best investment strategy with minimum risk possible.

Well, if you are planning to invest in stocks, we’d never recommend relying on these technologies because the stock market is a game dominated by human intelligence.

3. Process Automation

Automation has become the need of today’s emerging financial sector. Since there is no room for errors in the Financial industry, business owners look for algorithm-based technologies to take over the tasks.

Chatbots, paperwork automation, and employee training gamification are some of the best examples of automation in the finance industry. Process automation primarily uses machine learning that enables the finance and insurance companies to enhance their customer experience, minimize costs and scale up the services.

4. Secure and smooth transactions

Machine learning is a reliable technology to detect transactional fraud. The algorithms can track and analyze millions of data centers that is, of course, impossible for humans. Moreover, Machine Learning also minimizes the count of false case rejections and improves the real-time approvals. 

Artificial intelligence associated with machine learning is a boon, not just for the financial sector but for the Finance and Accounting market as a whole. According to recent research, the recovery cost for a $1 fraud by financial institutions is close to $2.92. ML-powered software and algorithms can quickly identify and mark events such as fraud or money laundering in transactions.

5. Customer Data Management

Lawful management of customer data has always been a headache for the financial industry. The massive amount and structural diversity in the financial data extracted from mobile communication, social media activity to transactional details, and industry data all come together, complicating the data management process safely and securely.

With machine learning and artificial intelligence, it can be done at ease. Since most financial institutions use clouds to store their data, machine learning can be integrated there to manage the sheer volume of data. 

Artificial intelligence can be used as a data analytic tool that can simply analyze the large volume of data sets to process data mining and bring valuable insights for better business operations.

Hopefully, this post has helped you understand the possible uses of how machine learning and artificial intelligence are coming together to shape the financial industry. The next focus of these technologies will be creating a completely automated but secure financial system.

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