Use cases of ML in Azure Databricks for the finance sector!

Image result for ml in finance sectorMachine learning in finance sector:  Machine Learning has penetrated almost every industry and is being extensively used for carrying analytical work which helps the company make important business decisions. Talking about the finance sector, it is a huge industry consisting of insurance, banking, real estate, etc.  
There is always data involved with any business and if that data is harvested and put to proper use using Data Analytics and Machine Learning techniques, we can generate great insights that otherwise are not possible by any means of manual data inspection. Now, before we understand what is Azure data bricks and why to use them for our purpose, let us first understand the use cases of ML in the finance industry. 
Use Cases in Banking Industry
Customer 360: Analyzing customer data to understand customer preferences and what type of banking service they might be interested in. This helps the industry design offers and promotion strategy that attracts customers to avail baking related services.  
Banking risk management: Risk analysis for baking sector using past data transactions. Loan default analysis and prediction are used by banks to determine whether a person should be provided any kind of loan or not or of what amount should be provided. This can be determined by analyzing the past data and transaction history as well as his assets and occupation-related data. 
Banking fraud detection: Frauds are very common in online banking transactions and since the majority of the retail market is moving to eCommerce, there is a spike in the number of online transactions in recent years. This has also given rise to online frauds. Machine learning models when trained with data of fraudulent transactions can identify whenever such transaction occurs. This use case is also heavily leveraged by eCommerce companies. 
Credit Scoring models: A credit: score is a numerical measurement based on an analysis of a person’s credit files to determine his creditworthiness. This score is used to understand the financial capability of a person. It is majorly used by banks and real estate companies to determine the creditworthiness of a person before making any financial or asset offer to them. Credit scoring systems undertake plenty of different factors to measure the credit score of a person just like a loan default identification system. 
Use Cases in Insurance Industry: 
Insurance Claims Automation: Claiming insurance is a very complex process as it undergoes rigorous inspection of the situation before a claim in process. Using the customer-centric data, this process can be automated, and the ML model’s outcomes can be used to decide on claims. More the data is used to train such models, more accurate results can be obtained any many different aspects of the insurance industry can be automated.  

Actuarial science (risk analysis in the insurance industry): Actuarial science is a methodology that uses mathematical and statistical techniques to assess risk in the insurance industry. Machine learning can be used here to train models which can then detect the changes in data and analyze potential future risk for the industry. It is much faster and accurate than manual actuarial analysis. 

Case Management: Process automation of data, document classification and clustering, analytics, and visualization, all can be done by building a pipeline of ML models. All these aspects of case management can be automated, and a model can process these tasks automatically as soon as case-related data is available.  

Personalized offersRecommendation systems are very well known these days in almost every industry. They track the user activity from the browser or mobile app and that data is then used to show related lucrative offers and promotions to the user. Another term for it is targeted ads and marketing.  

Use Cases in Stock Market 

Alternative data: Data is used by investors to analyze the performance of a company which can then be used for investment-related decisions. Purchasing stocks or investing in a company needs an analysis of the company’s performance. This can help make decisions related to what types and how big the investment can be made to ensure it turns into profit in the future. 

Back–testing: It is a very renowned method that involves the use of predictive models on historical stock market data for stock price prediction. Many people invest in the stock market in large or small amounts. Prices of stock are changing every second which generates a huge amount of data. This data is leveraged using big data technologies and machine learning to predict future prices of stock. This helps in buying and selling the stock. Azure Databricks is one such platform that enables building ML models over a large scale of data using parallel processing engines such as Spark.  

Trading cost analysis: Data of trading orders can be analyzed to predict cost and other parameters and performance analysis. This type of analysis by a company can provide meaningful insights to its clients and help recommend trading options to them. These types of models can be set up as a software-based service for the end-user operation and use.  

Conclusion 

All the above use cases are real-world applications in the finance sector and its industries where ML and data science are currently being used extensively to leverage profit and improve customer/user experience. Since the platforms where these applications are done are so large that the ML systems need to work and process a very large amount of data. To run models on such huge data requires high-end hardware resources which is not possible on a single machine. This is the reason why using Azure Databricks can help. It is a cloud-based tool for ETL purposes and allows to run of several types of ML applications on its cluster. 

Azure Databricks uses the clustering method. Clusters are a set of hardware resources that are assigned to a user for running the models. It is built with the integration of Apache Spark which allows parallel processing of multiple ML processes and provides the output in seconds. Also, the integration of Databricks with Azure cloud helps seamless connection with visualization tools such as PowerBI. This can help directly build visualizations from the outcome of models and can then be used for business decisions with clients and stakeholders.  

Dishant Modi

Data scientist intern

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