Last updated on December 1st, 2019 at 01:56 am
In financial sector, machine learning plays an indispensable role. Financial firms are searching for Data Scientists who can apply Data Analysis strategies to enhance their market performance. Since, complexity of data is growing day by day so industries and firms need such solutions which cope up with this and come with expert solutions.
Machine Learning in Finance
The main challenge for a Data Scientist is to understand the market situation. Finance can be categorized into different domains like, banks, stock, and real estate. So, the required programmer must understand these categories precisely this would help them in better Data Analysis and pre-processing.
To provide a wider view just think about the stock prediction. Various parameters includes OPEN LOW, HIGH, VOLUME, CLOSE, Dividend, Equity, Market Talk, News Feed, Twitter Feed and Balance Sheet. If the Data Scientist knows these features in a better way it would be easier for him to understand the data for further processing.
Machine learning has proved itself a useful asset in almost every field giving its tech advantage over other methods. It has achieved state-of-art accuracy which exceeds human-level performance. It is implemented in applications like self driving cars, stock market, betting prediction, bio-informatics, text-categorization, image classification and hand written character recognition.
Nowadays, Industries are opting machine learning techniques with a variety of tools, so of which use electronic components that were probably designed on similar software to what can be found at Upverter to power these amazing machines for measuring high volume of data, accurate historical records, and investing strategies for the finance world. With the invention of some high computation libraries like Tensorflow, Keras, Theano Pandas, NumPy, SciPy, and scikit-learn. Financial and time series data can be analyzed and processed easily with these libraries. Most of the libraries are open source so huge community support is provided, while there are other ways of using machine learning in regards to how to make a bot for use within the financial world.
Here we go for main applications of machine learning in finance:
The risk of financial fraud is increasing day by day.Every year financial industry goes through $80 billion loss due to financial frauds. There are different kinds of financial fraud like card skimming (stealing information from a skimming device), identity fraud (impersonate information), phishing (tricky online fraud), and Counterfeit (stealing card’s information). So finance providing companies are much concerned for defending their clients against fraudulent. For this sake, there are many rule based anti-fraud systems, software and tools to stop a fraud automatically. If you are in need of help with preventing or stopping online attacks, check out help websites that deal with pharming attack detection and removal, for you and for your company if you have had a recent breach of security.
These systems use different set of rules depending on their area of use but these transaction rules are not enough to stop the fraud cases in real time data streams. Thus, Machine Learning techniques are used to address these problems. Different algorithms are build by applying complicated logics and are trained using large data set of accurate information. Features such as name, age, gender, fingerprints and facial all contributes in detecting fraud probability.
Stock Market Prediction:
Stock market prediction plays a vital role in finance market. Many people chose to invest in finance but, they may loose more than they invested. This is because the volatile nature of stocks is too difficult to understand as well as to predict the movement trend of stocks. To make profitable decisions machine learning algorithms like Bayesian ridge, Random forest, linear regression or logistic regression are applied.
Training of model is done with past data and after tuning, best algorithm is applied. The best algorithm is judged by comparing the accuracy achieved by using different algorithms. These machine learning techniques can predict the spikes and returns of stock market. This automatic system predicts the best results for investors and marketers.
Financial services vendors are adopting AI and Machine Learning applications for growing institutional services. For improving customer experience financial sectors are using mobile apps through which user can have hands-on experience of account details and online transactions.
Here all in-depth management of account and transactions are managed through proper Data analysis. In another way, some financial sectors are using an AI chatbot. Deploying machine learning algorithms in chatbot provides auto-response to the clients. It uses NLP (Natural Language processing libraries) to process and respond to human voice. Chatbots can be 24*7 available to the users and make response from the database of each user. This information is accurately processed by algorithms.
Financial Services Recommendation:
Recommendation systems are serving as a handy tool for providing better financial services. Content based, collaborative and hybrid filtering helps user to know more about different apps and products of finance in market. It also helps in recommending the products in the social sites. Recommendation system helps in boosting long tail services. Such boost aids in bringing the hidden products into the light. Sometimes better products are hidden in this golden era of Internet. Overall it leads to growth of industries and customers are provided better knowledge and services.
Portfolio management deals in investment management and its risks and opportunities. Based on the economical factors, inflation and deflation decisions are made accordingly. Machine learning strategies can find the insights of data to identify repeatable patterns and structures. These insights are very useful for making investment plans to be developed.
In the nutshell, one can say that machine learning products turned out to be a better technique than traditional methods. Not only in finance but it has proved itself an applicable technique in every field. It make insights and decisions based on complex data solutions which is a great advantage for today’s world. In financial world, traders, investors and marketers are applying its modern approach to get profitable results. Machine learning models has the potential to grow and develop different applications for market and industries to capture new opportunities. Based on all the above facts we can say that machine learning plays an integral role in financial ecosystem especially when high scientific and computation libraries such as pandas, numpy and yahoo-finance are available in todays world.
About Gunjan Dogra:
Gunjan Dogra is a Data Scientist in Webtunix Solutions Private Limited. She writes articles for her passion and knowledge sharing. Her articles have been published in the number of reputed sites. A good thing about her article is that she includes multiple perspectives on a particular topic and draws a constructive conclusion.