Machine learning in finance has seen a massive rise in popularity during recent years. In simple words, machine learning in finance is all about importing large amounts of data and learning to perform particular tasks by learning from the data. It is making use of various techniques to manage massive volumes of information in the form of data. It also uses statistical models to get insights and make predictions. Head on to this link to get more info about machine learning solutions.
What is Machine Learning in Finance?
Management of massive data volumes through computer systems is also known as data science. There are many applications of data science in finance, like carrying out credit scores, managing assets, and analyzing risk. Machine learning has been a great fit for the financial industry because of its capability of handling large amounts of data with historical financial records.
Because of this capability, top banks and companies dealing in financial services have deployed AI and machine learning as a service. The reason is, of course, the automation in the various process which were sluggish and prone to error when performed manually. It has also helped finance-related businesses to decrease underlying risk and underwrite loans.
Advantages of Machine Learning Applications in Finance
There are various advantages of machine learning applications in finance. Many businesses and companies are using AI and Machine Learning in full throttle to get the maximum out of their business using these technologies. Some advantages are listed below:
- Process automation
- Reduced operational costs
- Almost zero chance of an error
- Enhanced Productivity
- Better user experience using computer vision
- Improved compliance
- Reinforced security
In addition to these significant advantages, there are many benefits of implementing machine learning in the finance industry. There is a massive number of machine learning algorithms and tools which are compatible with financial records. Due to the stability of finance companies, there is no restriction of funds to set-up high-quality infrastructure of hardware for enhanced functionality and efficiency.
As we all know, the finance industry is quantitative in nature. Finance experts have a great focus on maintaining and saving large amounts of historical financial data, which is a great fortune in the case of machine learning. Machine learning technique is deployed through computer systems to learn the process using previous data.
As there are large amounts of financial data available, this means systems backed with machine learning algorithms would get better with time. In the case of machine learning, the more, the better is a fact. More data means more learning of the system, which would ultimately give advantage to the organizations. It would also help in enhancing many factors and processes in the financial domain.
Many companies have taken note of this development and are working to implement machine learning applications in finance. They are investing massive amounts of money in research and development for machine learning to make it more useable for the finance industry. And for those who are still not interested in it, they would find their businesses in hot waters after a few years.
9 Best Use Cases of Machine Learning and Data Sciences in Finance
To highlight the purpose of using data science in finance, we have selected some use cases to discuss in detail and elaborate on how machine learning is turning out to be a fortune for the finance industry. These use cases are listed below:
One of the most significant responsibilities of financial services providers is to decline any fraudulent move against their clients. They have to bear more than 250% of the cost lost due to fraudulent activities against their clients in terms of recovery and relevant charges. To avoid such huge loses, these organizations can use machine learning software for fraud detection.
They won’t win their campaign against financial frauds using outdated and obsolete techniques and approaches. However, it is possible by incorporating machine learning applications in finance domain. They can use sophisticated software solutions backed with machine learning to identify and prevent fraudulent transactions.
These solutions are capable of analyzing massive volumes of data. This analysis enables software systems to recognize patterns and process predictive analysis. Thus, machine learning algorithms used in these solutions can restrict fraudulent transactions with a high accuracy, which won’t be possible by using AI only.
Many companies are using machine learning to reduce the losses due to financial frauds, while others are working fast to implement it in their systems to take advantage.
Risk management is also an essential responsibility of financial institutions, and service providers are supposed to do risk management. They depend on accurate predictions for the success of their businesses. Therefore, it is absolutely necessary for financial institutions to process current data in order to identify trends and accurately forecast emerging risks.
Conventional software systems used in the financial domain are capable of predicting creditworthiness on the basis of static data imported from loan applications and recent financial reports. However, machine learning technology is far more advanced, with a whole lot of possibilities. Machine learning algorithms can recognize live trends and relevant factors that could influence the ability of the client to make the payment.
Risk management is also connected with the prevention of financial fraud and crisis prediction. Machine learning financial services are capable of addressing these and many other relevant issues in order to manage financial risks. That is why a considerable number of financial institutions were already emphasizing on the implementation of machine learning-enabled solutions in their existing systems.
The hedge funds have diverted from old-school prediction methods these days. The use of machine learning in predictions of trends of the fund has seen a huge rise. Hedge fund managers can easily recognize market inclination probably a lot earlier than it was possible with conventional investment analysis models.
Major financial institutions have taken the potential of machine learning to interfere with the investment banking industry, and therefore, they are working to develop automated investment advisors or Robo-advisors backed with machine learning technology. JP Morgan, Bank of America, and Morgan Stanley have achieved considerable success in this venture. Other companies are also likely to follow the footsteps of leaders.
The security of financial data has been a huge concern for financial institutions. The number of security breaches has also increased considerably in recent years. The task of identifying modern cyber-attacks can’t be restricted using obsolete security software.
This is a challenging situation and requires advanced counter technology. The security solutions backed with machine learning are amazingly capable of serving the purpose of security of high-value financial data. These solutions have the ability of intelligent pattern analysis in combination with big data operations.
This gives machine learning security technology an upper hand over conventional security software solutions. This is why a lot of companies are investing in advanced technology machine learning that enables data security solutions to make their valuable data safe from cyber-attacks.
Loan and Insurance Underwriting
A considerable number of insurance companies are turning their heads towards machine learning to identify risks and set premiums. Machine learning is capable of making predictions on the basis of historical patterns and on-going trends, that is why it is the perfect tool for insurance companies to enhance their revenue and profits.
The banking sector is also getting a huge advantage through the use of machine learning technology. Financial organizations that offer insurance products and loans to their clients are also getting benefits because of machine learning in a similar way. Regardless of the insurance product, whether it is loan protection, health, mortgage, or life insurance. Machine learning is able to cut the chances of underwriting risks.
Algorithmic trading is supposed to automate the process of trading by performing trading action in accordance with existing criteria defined by the user, which could be a trader or fund manager. In short, algorithmic trade is capable of executing purchase or sale of a stock quantity whenever price-per reaches an ideal or particular value.
With the incorporation of machine learning in algorithmic trading, various new tools are available to make algorithmic trading more than just an automated process. It turns algorithmic trading into intelligent trading.
The machine learning algorithms are designed in such a way that they are capable of analyzing historical behavior of markets, figure out an ideal market strategy, making trade forecasts, and much more. Even AI is not capable of giving such value without machine learning.
According to recent estimations, it was found that around 2% to 5% of the Global GDP was laundered annually. Banks are not capable of winning the battle against this unethical and immoral activity.
This problem could be addressed with the help of advanced machine learning technology. It has the ability to identify patterns that are closely associated with money-laundering practices. Machine learning applications in finance are proved to be a great help in the detection of money laundering patterns, reducing the number of false positives, and easier compliance with regulatory authorities.
Commerzbank is working on automating 80% of its compliance checklist processes through machine learning till the year 2020. The process will be done through shifting the focus of AI towards money laundering.
Financial consumers often make complaints against poor customer services. They want accurate information and fast processing for the solution to their problems regardless of whom they are talking with, whether it is a virtual assistant or a human operator.
AI chatbots are being used for long for customer services, but customers aren’t satisfied. A lot of consumers complain that it doesn’t look like their problems are being understood while talking to chatbots.
Machine learning brings a whole new era of virtual assistants and cognitive services who are capable of learning instead of following a predefined set of instructions. Chatbots powered with machine learning adapts their serving strategy in accordance with the behavior of individual customers. This ultimately gives a whole new experience of customer services to consumers, which is enhanced and more comfortable.
Check out this free speech to text software
The process of payment transactions and purchased security following a stock trade is termed as trade settlement. Electronic transactions are an instant way to complete trade settlements and are being used for long, but the trade isn’t always like it should be. A number of factors could limit the accomplishment of trade.
The use of modern trading platforms and regulatory requirements have considerably reduced the number of trade failures. But while handling high trade volumes, trade failures can still influence the efficiency of the trading system. A challenging task is to resolve failed settlements manually, which takes a considerable amount of time.
However, with the use of machine learning solutions, the cause for failure of a trade could be identified instantly. The machine learning applications in finance are also capable of providing solutions in a matter of seconds. Machine learning technology can even predict the trades which are likely to fail. So, machine learning is a great way to handle failed trades in a fraction of seconds.
Folio3 Machine Learning Financial Services
Folio3 always do intense research and take up challenges to meet the requirement of clients and cater to the needs of various verticals of the industry. Here are some applications of our machine learning financial services for industry:
ATM Cash Forecasting
A multinational bank that is also regarded as the largest bank in Pakistan on the basis of assets is a client of Folio3. Our valuable client approached us to address their problem regarding ATMs (Automatic Teller Machines). The bank operates over 2000 ATMs across the globe. They wanted to get a solution with the capability to predict the cash-flow management for such a large number of ATMs.
They asked us to develop a state-of-the-art atm cash forecasting solution for them to address the issue. Our talented developers worked on the requirements and suggestions of our client to come up with a cutting-edge solution. This intelligent solution helped the bank in managing the cash flow for ATMs and raised the profits by 6%.
The highlights of our sophisticated ATM cash flow management solution are the optimization of ATM cash management, which helps the bank to avoid situations like out of cash and overstock. Our solution provides automation, automated analysis of past transactions enables our system to predict the required cash in individual ATMs. It also provides timely reports and notifications to predict the pattern of cash withdrawal.
Our system is capable of forecasting patterns of cash flow and real-time data, which ensures the availability of cash and, ultimately, customer satisfaction. The technology used explicitly in the development of this sophisticated solution is scikit-learn.
Customer Churn Prediction
A leading tech company in Pakistan is our client. This company deals in high-end services for clients in various countries. They needed a solution capable of predicting and understanding the behaviors of customers. After analyzing the needs and suggestions of our client, we took the responsibility to develop a high-end predictive learning system. The solution was able to recognize the customers who were not willing to pay dues and the ones who won’t renew the subscription of SaaS solutions. In addition to working on user’s data, the system is aimed to work in cooperation with owners and marketing team to understand various underlying factors which could help in designing better campaigns.
Our churn prediction solution helps our client in quantifying the loyalty of their customers while facilitating the reduction of churn with the help of data. The highlights of our solution are customer segmentation through advance data science techniques for dynamic user segments and evolved client base.
These highlights also include predictive attrition for an enhanced customer retention rate and improved data management activities. The statistical analysis helps our client in recognizing the underlying factors and designing better campaigns. Through these highlights, our sophisticated customer churn prediction solution improves customer retention.
We used technologies like Apache Kafka, Yarn, Spark, and Zeppelin to develop this cutting-edge solution for our client. To get more detail about this solution, head on to the given link
3 Best Machine Learning Applications in Finance
In this section, we will discuss some great machine learning applications in finance, which have made various aspects of the finance domain better. These applications are listed below:
Streamlined Claim Handling Process
There are multiple challenges for the insurance industry. One of the limiting factors in the insurance field of the finance domain is the longer time span taken to process the claims of the insured person or organization. As the insurance company’s personnel gets involved in the claim process to investigate and assess the situation for claim processes.
This sluggish process could be a huge roadblock for an insurance company and the whole industry. To avoid complications, machine learning and artificial intelligence algorithms are deployed to streamline the claim processes in the insurance industry.
Whether it is a simple car accident or destruction of crops due to drought, the aftermath of an insurance claim could take weeks otherwise. However, machine learning algorithms can be employed to examine the situation and make quick rational decisions about the early payouts, which would surely satisfy the consumers. Thus, eventually creating a trusted and strong bond between insurance companies and consumers.
Internal Workflow Automation
Inspecting massive data written in hard form in a short time is almost impossible. Loan and Insurance organizations don’t have much time to engage their employees in extracting data from papers and then transfer it again on papers.
This could be a massive roadblock for financial organizations. However, going through the data of consumers is also necessary to gain substantial information about the consumers for a secure business.
Therefore, machine learning algorithms are deployed to automate the internal workflow of financial organizations and to engage the staff in more productive work. Machine learning algorithms have the capability of analyzing the data written in hard form and converting it in soft form for further use. These algorithms also predict the risk rate while analyzing the data extracted and aid in making quick decisions.
There could be various factors to drive the performance of the stock market. One of these factors is the behavior of investors and how they respond to rumors or news floating in the market. AI machine learning algorithms deployed by various trading agencies import massive data about historical transactions and analyze the current situation while keeping in view the various types of news influencing the market to predict the behavior of traders and investors in the stock market.
There could be news like natural disasters which could impact the market on a larger scale, the machine learning algorithms deployed to analyze behavioral finance predicts the future outcomes and helps trading organizations to act accordingly to get benefit from the market and avoid incurring any loss due to these situations.
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