Is Machine Learning the best way to grow a FinTech company?
Finance is something that no person on earth can live without. It is the basic necessity of life, as everybody needs money to eat, travel, and buy things. Although as technology gets smarter so do people. The present financial market is already comprised of humans as well as machines. People are finding more and more ways to default on loans, stealing money from others account, creating a fake credit rating etc.
Today, machine learning plays an integral role in many phases of the financial ecosystem. From approving loans, to managing assets, to assess risks. Yet, only a few technically-sound professionals have a precise view of how ML finds its way into their daily financial lives. Nowadays, detection of frauds has become easy thanks to Machine Learning. Recent advances in technology have enabled financial institutions to explore the applications of machine learning techniques in areas like customer service, personal finance and wealth management, and fraud and risk management.
What is Machine Learning?
A lot of people have probably heard of ML, but do not really know what exactly it is, what business-related problems it can solve, or the value it can add to their business.
Machine learning is the science of designing and applying algorithms that are able to learn things from historical data. It was born from the aspects of pattern recognition ML explores the study. And construction of algorithms that can learn from and make predictions on data. This allows ML programs to respond to different situations even though not being explicitly programmed.
ML has given us ample amounts of use cases like self-driving cars, product recommendation engines, predictive analytics, speech recognition to name a few. Increasing reduction of human effort is the main aim of data scientists with implementing ML. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. It wants to bring down the time that humans take to read, understand, analyze the big data to a few seconds.
Characteristics of Machine Learning in Finance
A human brain if pushed to its extreme can be able to do only a few tasks at the same time. Whereas machines having no boundaries can concentrate on several thousands of tasks. Some of the reasons why we should use machine learning in Finance are:
Reliability: When it comes to handling finance, establishing trust on the person is essential. Since banks, investment firms, stock markets do not transact a few dollars every day, it is imperative that we have trust in the firm or person handling it. Machines embedded with ML are corruption free and will complete the requests provided.
Speed: Speed is essential to make money while trading stocks in the stock market. A mere few seconds miss would result in losses of millions. Machine Learning algorithms are able to provide accurate in-depth analysis of thousands of datasets in a fraction of a second.
Security: With the recent hit of WannaCry ransomware attack, it is clear that we are still prone to hacking and cyber security theft. Machine Learning helps solve this by categorizing its data over three categories. Then builds models which are an essential step to predict fraud or anomaly in the data sets.
Accuracy: Human nature shows that doing the same mundane tasks results in less focus and quality of work. Moreover, machines can perform repetitive tasks for an infinite amount of time. ML algorithms do the dirty work of data analysis and only escalate decisions to humans when their input adds insights. Also they help find even the minute patterns which are helpful.
Advantages of Machine Learning in Finance:
Fraud Detection:
Financial service providers have no greater responsibility than protecting their clients against fraudulent activity. Financial fraud costs Americans, alone, $50 billion annually.
Fraud detection process using machine learning starts with gathering and segmenting the data into three different segments. Then machine learning model is fed with training sets to predict the probability of fraud. These datasets are found from historical data. Lastly, we build models as an essential step in predicting the fraud or anomaly in the data sets. By comparing each transaction against account history, machine learning algorithms are able to assess the likelihood of transaction being fraudulent.
Unusual activities, such as out-of-state purchases or large cash withdrawals, raise flags that can cause the system to introduce steps to delay the transaction until a human can make a decision. Since the use of machine learning is still small and growing it will in a few years evolve a lot more and be able to detect complex frauds.
Customer Service:
Poor customer service still remains one of the main complaints, regardless of any industry. Originally, the complaints centered on slow customer service, but with the universal utilization of automated phone support, customers are frustrated by not being able to speak to a human.
Now we can make use of chatbots in several service industries. For example, financial companies can reduce their customer supports workload by having the bot to answer the FAQs and other queries. Also, the bot would be able to handle millions of queries at the same time and that too working 365 days a year. Therefore chatbots provide a good opportunity for small companies reduce their expenses and help in the growth of revenue of the company.
Stock Market prediction:
Everyone wants to get rich by simply buying stocks. But are you buying the right stocks? The ones that are actually going to increase? Well yes, it is very tough to know unless you have done a lot of technical analysis on each stock you wanna buy.
To solve this, Machine Learning algorithms make use of historical data about the company like balance sheets, profit and loss statements etc. And finally, analyze them to find out meaningful signs regarding the future of the company.
Further, the algorithm can also hunt for news about the company. And learn from sources around the world regarding how the market feels about the company. Also, with natural language processing, it can scan through the news channels and video libraries of social media to find more data about the company. Thus helping people with knowledge on stocks to accumulate wealth safely.
Virtual Assistants:
We all require the help of someone in order to complete the tasks we need to, be it with the help of google or a human. With the help of Machine Learning enabled digital assistants, executives and managers to perform their jobs with greater ease than ever before.
Google’s Allo, Apple’s popular Siri, Facebook’s M, and Microsoft’s Cortana currently represent the state-of-the-art in digital helpers. Machine Learning technologies includes several functionalities that can be useful for developing a custom digital assistant such as Speech recognition, access to big data, powerful analytics capabilities and ability to interact on social media etc.
Whether a financial service company chooses to invest in the development of a virtual assistant platform for its own operation, or for the purpose of offering the platform as part of a service package for their clients, the return on the investment is likely to be substantial.
In conclusion, although machine learning is a newer technology there are lots of academicians and industry experts among which machine learning is very popular. It is safe to say that there are a lot more innovation coming in this field. And adopting Machine Learning also has its own setbacks due to data sensitivity, infrastructure requirements, the flexibility of business models etc. But the advantages outweigh the drawbacks and help solve lots of problems with Machine Learning.
Finance is a very critical matter in all the countries around of the world, and safeguarding them against threats and improving its operations would help all grow and prosper faster. Hence, improvement in finance with technology is crucial to having a safer and secure economic world.
