Advances in Financial Industry with Machine Learning

Today, the financial sector is more interested in machine learning technology for it can bring the significant change from the bottom line. Machine learning based financial systems are expected to catch costly errors, accelerate the process, and augment the decision making process.

Let’s investigate more.

Machine learning [ML] technology facilitates the systems to learn from the data, illustrations, and experience instead of following the explicitly programmed set of rules. Thus, systems are made to carry out the complex processes fast and accurate.

Henceforth, in recent years we witness much discussion, experimentation, hopes, and research on machine intelligence for the benefit of mankind. Evidently, we find significant advances in the machine learning capabilities in every field including health, productivity, cancer treatment, astronomy, customer experience, research, physics, and, more.

Let us understand the significant inroads of machine learning in finance sector here.

The financial service industry has an enormous dataset, say in petabytes. Further, it has a larger amount of data input every day along with heaps of historical data. Rightly, the open-source machine learning algorithms and tools could be efficiently used in financial data.

ML plays a key role in reducing the operational costs, better productivity, enhanced user experience, compliance, and, security.

Applications of Machine Learning in Finance:

Automated Process:

Machine Learning helps in automating the process and helps to replace the manual work for repetitive tasks increasing productivity and eliminates human error. The use of chatbots, automated call center and paperwork, gamified employee training, and, etc., are cutting the costs to an appreciable extent.

For instance:

Machine Learning technology is able to access the data, follow and recognize the pattern and interpret behavior. This could be readily used for customer support system that can mimic a human agent and solve the customers’ unique queries.

Fraud Detection:

The machine learning algorithms are precise and can spot any fraudulent behavior in split-seconds. The algorithm is able to detect the actions or attempted activity carried out by the user and can spot any suspicious activity easily.

If there runs a suspicious pattern of usage that deviates from the normal pattern, it would ask for more authentication process or even block the card.

Further, in the context of security, it detects the micropayments in large quantities [if any] and flags as smurfing.

Credit Scoring:

It is a statistical analysis conducted by the financial services or the lenders to access the candidate’s worthiness. Depending on the credit scoring value [ ranges between 300 to 850, where 850 is the highest rate], extension or denial of credit for a candidate would be decided.

With the historical data of the consumer, the ML performs the credit-scoring task on real-time efficiently.

Algorithmic Trading:

Machine Learning monitors the trade results and news in real-time and analyzes thousands of data simultaneously. It detects the patterns of stock prices and predict the probable share market status in near future and proactively sell, hold, or buy stocks.  

Portfolio Management:

We find robo-advisors more prevalent these days, especially in finance. The online wealth management services or the portfolio management uses the statistics and algorithms for managing the client’s assets.

Depending on the current assets, the robo-advisor helps the user with the investment opportunities for the desired goals against possible risks preferences.

Moreover, it can suggest personal insurance plans too. The days have come that the people prefer robo-advisors in place of personal financial advisor for lower costs and accuracy.

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Use cases of Machine Learning in Finance:

In a case study, J.P. Morgan Research built an algorithm based on some 250,000 analyst reports that provided the source material for learning the implication of financial terms such as “overweight,” “neutral” and “underweight.” The team then tested the model on 100,000 news articles that focused on global equity markets with a view to informing future equity investment decisions.

BNY Mello witnessed operational enhancement through integrated process automation for their banking ecosystem and was responsible for $300,000 in its annual savings.

Challenges of Machine Learning in Finance:

Acceptance: Still, machine learning adoption is not clearly understood by everyone. Not all fund managers and investors entirely depend on machine learning for asset management.

Dynamic Nature of Finance: Finance market as expected is changing constantly. ML working models best suits for stationary data and there is lots of research required for non-stationary data.

Walk forward Optimization: Evolving data-distributions is a real challenge with the existing models. Though the researchers do not incorporate look-ahead in the research, the data distribution is evolving at a faster rate and may not give high accuracy rate always.

Small sample size and quantifiable data: Automated learning approaches are not applicable when the data is too small. It needs a lot of historical data to remove the doubts and adhere to the quality.

In addition to these challenges, when the price value has to be determined, the economic cycle matter a most. And, this keeps changing always unpredictably.

Way Forward in Machine Learning:

Machines may not immediately surpass the human ability. Recommendation systems are more helpful in financial predictions with machine learning.

An innovative approach to serve the clients is the much-needed strategy for a financial business to succeed. Using the data has reached either success or is still in its nascent stage. And, today’s technology landscape demands and promises more.

The success rate of a machine learning dependent project is greatly influenced by the infrastructure, collection of enormous and suitable datasets, and application of appropriate algorithms.

Bridging the technology gap between the systems would promise a better future. And, the days where we can see machine learning everywhere are not so far.