AI/ML, New finance fundamental

Introduction

What is machine learning?

Machine learning is a branch of science that inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. In short other terms, machine learning is a type of artificial intelligence that enables machine to learn by itself via varies methods like supervised and unsupervised learning.

What is artificial intelligence (AI)?

Artificial intelligence is the capability of a computer system to mimic human behaviour such as learning and problem-solving. Al use complex model in order to mimic or act like a human.

?What is Finance?

Finance is term as discipline which related to money, currency and capital assets. It is related to, but not interchangeable term with economics, the study of production, distribution, and consumption of money, assets, goods and services. and include a process of funding , saving and investing.

How Artificial Intelligence/Machine Learning facilitates finance?

Below are some area where the AL/ML are being uses in finance sector

• Algorithmic trading

Algorithmic trading refers to the use of algorithms to make trade decisions more efficient. Usually, traders build complex models that monitor business new trends and activities in real-time to detect any factors which indicated emerging new demand or factors that can hinder business or trade. The model comes with a predefine set of instructions on various parameters – such as time, price and other factors – for placing trades without the trader’s active involvement. Unlike human traders, Artificial Intelligence/Machine learning makes fast decisions and save time , which gives human traders an advantage over the market average.

• ?Customer Satisfaction Prediction

Customer satisfaction is a important thing to look, how products and services offered by companies and organizations meet customer expectations or not . This can assists companies in managing and monitoring their business effectively because in modern era take their customer taste and preferences and act accordingly is a key to success. You can start by predicting the review score for the purchase done by customer. You can use simple AL/ML algorithms like Na?ve Bayes, Logistic regression, Random Forest.

• Share Market Analysis Using Simple Machine Learning/ Artificial Learning Techniques

Developing sound understanding of Share market is the complex and sophisticated. Brokerage and banking firms rely on the stock market to generate revenue and manage their risks. However, the business model is complex due to the volatile and dynamic parameters of global economics, which change every day. Machine learning models can be used to simplify this task with high accuracy. For this project, you can apply simple machine learning techniques and algorithms to represent the stock market patterns and plot the graphs to understand the risks for a particular stock, which can help one make better stock investments decisions. You can use Pandas and Matplotlib for plotting the data. The analysis can consist of plotting the moving averages for different stocks over different periods. Plotting heatmaps and cluster maps ( use seaborn modules) can also help visualize the correlation between different attributes. Use the Morning Star Dataset to implement this machine learning project in financial sector.

• Fraud detection and prevention

Fraud is a major problem for banks and financial services companies. Usually, finance companies keep a huge amount of their data stored online, and it increases the risk of a security breach via a ransomware or viruses. With increasing technological advancement, fraud in the financial industry is now considered a huge threat to valuable data like credit card numbers phone number attached to accounts etc.

It works by comparing a transaction against other data points – such as the customer’s account history, IP address, location, etc. – to determine whether the flagged transaction is contrast to the behaviour of the account holder. Then, depending on the nature of a transaction, the system can automatically decline a withdrawal or purchase or send a alert to authentic account holder.

Despite of such advancement AL/ ML give in finance sector, there are so challenges Finance Companies Face when Deploying Machine Learning/ Artificial Intelligence

?In terms of risk management and financial management business decisions, machine learning models saves time and give fast and accurate predictions, compared to traditional models. However, AL/ML models are complex, less transparent and required niche skill set which is leading to a set of challenges in risk management and model validation. Below are some main challenges while deploying AI/ML.

• Incompetency of Model Users Validators and Data Scientists.

AL/ML models are black-box in nature due to the complex calculations that are hard to understand and required a niche set of skills. Thus, there is a limited understanding of the output. Data scientists and ML mode lists do not have appropriate training, which is a significant shortcoming for financial institutions and banks that can lose their customers due to the absence of model explanation ability.

For instance, if a customer requires a car loan and a bank declines the application based on the artificial intelligence model then there is a risk that a customer may challenge the output and ask for an explanation of the denial. Therefore, in credit risk management, negative decisions should be explained to advocate the suitability of ML-based models.

• The Imperfection of Scalability Support

There is a multitude of processes involved in AI/ML workflow, including data analysis, manipulation, model training, validation, testing monitoring, etc. When data grows and processes scale up, there is a necessity to rearrange ML algorithms, which means that continues monitoring and maintenance are importance to get or generate right output. This, in turn, may be an hectic task.

?Conclusion

We can’t deny the fact that artificial learning /Machine learning is a key which will unlock new potential in finance sector, but we need to in depth research to overcome the short fall of the models or algorithms created using machine learning, in order to increase its efficiency. ??