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Machine learning in finance: how does machine learning transform financial operations?

Machine learning is a subset of artificial intelligence widely applied in financial applications and used to improve cost-effectiveness and overall efficiency of financial services. Implementing new technologies can help you gain a competitive advantage and reduce the costs of running the company. Learn how you can apply machine learning in your business tools and applications.

Powerful financial analytics, improved cybersecurity and fraud detection, reliable risk assessment, smart chatbot advisors powered by machine learning and natural language processing — all these solutions and many more can make your financial organization more flexible and mature than others. AI (artificial intelligence) and ML (machine learning) can improve your company productivity, reduce costs, enhance customer experience and make more data-driven decisions.

Machine learning usage in statistics

According to the Statista reports in 2019 71% of respondents claimed that they already deployed machine learning and data science in their work. The most popular application of AI in the financial industry in 2020 was fraud detection (58%) and increasing the efficiency of financial processes and analytics (41%). Artificial intelligence was also leveraged to improve the quality of customer care (31%). ML and AI were important as well in the process of personalizing financial products and services.

There is no doubt that there can be multiple, possible machine learning applications in finances. Besides, special data models and ML algorithms work wonders not only for the financial industry but for any other company too. After all, the efficiency of the processes, advanced cybersecurity, high quality of customer service, or producing useful business insights is crucial for the success of all organizations around the world.

How can machine learning to improve your company’s performance?

To gain a competitive advantage and outrun your rivals in the financial sector, you need more than just interesting products and services. Your organization needs to work like a well-oiled machine — your goal should be increasing efficiency in all departments. Of course, it is not something that you can achieve over one night. You can do it in stages. Plan revolution in your business carefully. After you implement one machine learning-based solution, you’ll quickly observe positive changes in your financial company. Let’s talk about some common use cases of ML in the financial industry.

Automating processes 

In all kinds of companies, when machines are taking care of manual, repetitive tasks, professionals can engage in processes that require the presence of an actual human and his creativity. Multiple processes can be automated thanks to machine learning. Special programs powered by ML and NLP can handle the paperwork for you, for example by collecting important information, producing documents, etc. You can also automate basic customer care by creating chatbots or voice assistants. 

The most important asset you save by leveraging machine learning in process automation is time. Apart from it, you can reduce so-called human error in administrative tasks and documents. Well-designed chatbots can significantly improve customer experience. ML-based automation can make your day-to-day work more effectively

Reducing costs 

It is mostly automation that reduces the costs of running the business. With chatbots and software that can handle tasks faster and better than an actual employee, you can simply reduce the number of workers that have to deal with manual tasks and invest saved money better.

Human errors costs — usually a lot. Outsourcing certain tasks to machines, allows you to keep the money that you often have to spend to fix mistakes. There are also bad investments to be mentioned. With powerful ML-fuelled analytic tools, you can assess the risk of a potential investment, predict the outcomes of investing and make better, data-driven decisions that will protect you from losing money.

Boosting security 

Machine learning algorithms can do an excellent job in detecting fraudulent transactions. ML-based solutions analyze an enormous amount of data and find patterns that would go unnoticed by a human. Artificial Intelligence quite effectively spots suspicious activity in financial companies’ systems. It learns all the time from clients’ and employee’s behaviors what is right and what is rather untypical. 

The best thing about ML-based technology is that it can spot weird activities in the systems in real-time, instead of detecting them after cybercrime has already occurred. Such software can react appropriately (by blocking the transaction, account, or informing a human specialist) to ensure the safety of the assets belonging to the financial organization, or its customer.

Improving customer experience

ML-based customer care solutions can improve the satisfaction of your potential and current customers on two levels: by providing necessary information in a short time and by helping them solve their actual problems. Many companies are nowadays implementing chatbots. Such an approach to customer service has many benefits. First, you reduce the queues on the phone line. Secondly, you ensure 24/7 customer care without increasing the expense of running the customer center and finally, a well-designed chatbot can turn a negative user experience into a positive one.

Chatbots can deal with complaints, sales, solve problems and inform your customers. To put it simply, they can do all those things that human customer advisors do, but you don’t have to pay them a monthly salary. Such a chatbot is a certain investment, but can also help you save a lot of resources and improve your customers’ experience.

What are the challenges faced by financial organizations that implement ML-based solutions? 

Implementation of artificial intelligence and machine learning in financial companies will for sure make you more efficient and competitive. This very process is not easy, though. Adopting solutions based on such a complex technology as ML usually comes with quite an expense. You will not only need to buy new software or build customized solutions but also pay for hiring data scientists and experienced developers that will assist you. You also have to train your employees to teach them how to use new applications and business tools. 

See also: Hiring the best machine learning developer
To actually gain from machine learning, you’ll have to ensure the high quality of data used for tasks automation and creating business insights for your company. There will be new processes to learn, new tools, requirements, and safety procedures. Leveraging machine learning is a huge step forward for traditional financial organizations. Implementation of this technology should be carefully planned, but don’t worry — with the small help of an experienced software house, it won’t be very difficult.

Will the importance of ML in financial companies grow in the future?

As machine learning is being leveraged by more and more companies from the financial industry, it is quite safe to state that ML in the financial industry will continue to grow. It seems it is just a matter of time before you’ll be forced to apply ML and various AI-based solutions in your business because of the changes in the market. 

Don’t wait for another to outrun you. We can help you transform your business with machine learning fast and smoothly. Contact us, if you’d like to learn more about how ML-based solutions can improve your financial organization.


cto - Chris Gibas

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