Have you recently heard about Fintech and the rise of AI ML applications in Fintech? If yes, this article has been created to accentuate the role of data science, AI, and ML in fintech services.
The Pareto principle or the 80-20 rule is often referred to as the beginning of the twentieth century, Pareto created a mathematical formula that defined the inequalities in the distribution of wealth in his native Italy known as Pareto distribution wealth.
In the recent months, we have been exposed to a growing threat from cybercriminals and ransomware agents who are using highly specialized technologies to break into our IT systems. These IT systems cover 90 percent of software and cloud platforms used by the global banking and digital currency systems. The rise of ransomware has come so quickly that it has taken companies ages to create potent market surveillance and fraud detection tools to stop these. AI ML and data science are looked upon as the new-age solutions to alleviate the pain caused by fraud and ransomware.
Let’s see how.
How does Machine Learning work in Fintech?
AI ML models for the financial services industry help in deploying automated platforms in a variety of banking and financial services operations (Fintech operations), including fraud detection, identity management, access and authority privilege management, encryption / coding, blockchain, and portfolio management. A major digital transformation operation created around Cloud modernization and customer experience management can help fintech services could leave gaps exposed for cyber threat agents to penetrate deep into the system via BYOD, IoT, and emails.
Using AI ML tools, there is a number of cases where threats have been found to be nullified- from innovation to development to production and final application, these AI ML tools for financial services security can be used with little or no coding at all.
Let’s learn how to create a simple ML model for financial services.
Here is a step by step process to create a ML based financial services algorithm.
There are many Auto ML tools that can help you build, train, and fine tune the best machine learning models. But as beginners, we will focus on creating a simple ML model and then move to AutoML tools.
Here’s how you need to build a simple ML model –
- Raw Data: Data preparation is the number one step to create an ML model for financial services. Data Science courses in Noida train professionals in this area. Raw data can be loaded to a tabular format and then processed to garner potential insights to drive ML models.
- Action Target: Next step is to target a tabular group of rows and columns to actually streamline the Predictive Intelligence insights for the ML model.
- Automation: At a certain stage, you would be using automation to detect anomalies in data segmentation and data cleaning would become necessary to continue the ML modeling. This would help you to choose the correct Machine Learning model and train tune in data for better predictive analysis.
- ML model and control: This is the most important step in turning your Machine Learning model into a full blown financial services management tool. It is often referred to as the “version control” ML Ops where ML experiments are carried out to manage larger data sets and fine tune ML models for persistence, scalability, and reliability. Once Big Data Predictive analysis is carried out, your ML Ops should be able to maintain transparency and version control.
- Deploy and monitor the model: Finally, once all the prerequisites are fulfilled, you can run the ML model with manual features or automated ones, depending on how long and how big your project is going to run.
You can test multiple ML Ops to optimize for hundreds of project parameters in order to accurately figure out the best ML predictive intelligence model for your data.
Role of Auto ML in Big Data Science Projects
You should focus to earn multi-layered security and version control for your Data Science tool for financial services. From encryption to dedicated VPN and authentication, your ML model can run on autopilot every time the fintech software is loaded.
Since we are handling a large volume of financial data in most cases, we ought to learn automation for machine learning version control. AutoML models follow a scientific approach that typically provides full visibility into model factors and variables.
If you are not ready to build a supervised ML model, you can leverage enterprise level AutoML tools, that eliminate the heavy lifting from your workload. You can also use ETL tools to fine tune your Big Data management and ensure that your Machine Learning model is the best data science workflow for tabular datasets.
