The analytics space is home to over 50 various software solutions that address challenges in different disciplines. These software solutions can be divided into two broad categories: licensed and open-source. Although licensed software provides exceptional customer support, open-source software, which has no license fees and is freely downloadable, has the advantage when it comes to community support, constant technological and analytical upgrades, scalability, and data science compatibility.
Python is an open-source software, and is the most versatile programming language used for analytics in the current marketplace. Consistently used for big data analytics, predictive modelling, machine learning, data architecture and automation, Python has become one of the most used open-source software in analytics thanks to its package-based structure and community support capabilities. Compared to its licensed software peers, Python provides diverse packages for analysing both structured and unstructured data. As unstructured data continues to proliferate, technocrats are highlighting Python as one of the most important software skills to master.
Qarar has used Python for application scoring for the first time in the Middle East, in partnership with a major bank in the Kingdom of Saudi Arabia. We began the consulting process by analysing the client’s vision, scale of execution and complexity of analysis. Due to both time and budget constraints for the application scorecard project, Python was naturally the software of choice.
Python, due to its package-based structure, assisted Qarar in effectively executing the project at the client’s premises. Data cleaning and preparation was executed using Python’s ‘pandas’ package, while data distribution analysis and validation was executed using its ‘numpy’ package and predictive modelling was executed using its ‘scikit-learn’ machine learning package. The scorecard proved as predictive as the scorecard developed using license based solutions.
Any organisation that wants to digitise cannot shy away from Python. Its ability to interact and upgrade seamlessly with web-based applications, mobile-based applications and big data technologies make it the most adaptable and scalable software in the analytics world. Organisations have achieved massive scalability from their Python-based scorecards by deploying them in big data technologies like Hadoop and allowing them to interact with their mobile and web-based applications. Whether you want to run a fraud scorecard on transactions or assess customer loan risk through an app, Python is the answer.
With such a scalable technology in place, banks and other institutions can take actions to reduce the impact of potential defaulters. With quick response time, lenders can reject loans from people applying through a mobile app or reduce exposure to the client instantly depending on the customer risk profile assessed through risk models. It can reduce the client’s credit limit exposure instantly, exclude the client from any credit limit increases automatically, and limit the oversell allowed on their credit card. Models can be developed over the entire credit lifecycle from application, bureau and behaviour scorecards through to propensity and attrition based models. Python’s package-based structure makes it easy for it to upgrade itself with new technological and analytical updates – a feature the telecom industry frequently uses to analyse streaming data.
Every lender and financial institution realises the importance of identifying the risk of an applicant, as profitability is dependent on the business of taking good risks. This can be enhanced by implementing effective methods and models for identifying good risks in advance. With the aid of risk based models and Python’s scalability, the power of these models can also be integrated into the complete credit life cycle of any risk product. The ability to assess clients in terms of their credit risk is a critical component of risk-mitigation strategies going forward, particularly in the Gulf region which is experiencing significant growth. However, once the ability is obtained through scorecards, the speed of execution and scale of implementation is critical in realising tangible benefits for the analytics developed.