Finance as a whole has definitely changed in the past couple of years. The usage of different, much faster applications and processes have sped up, digitalised and, sometimes even reshaped a bulky sector which needed a fresh change. Risk management for financial applications and loans, in particular, have been the micro category which has gone through the biggest of these changes. Let’s analyse which pieces of tech are being used and how this can (and will) become an industry-standard in the nearest future.
Automation In Processing Data
As many of you may know, data has become an incredibly valuable asset. In 2018, it has been stated how big data and data points became more valuable than oil, effectively becoming the most powerful resource on the planet. Automation in processing data refers to the process many fintech companies use to instantly assess whether if a user is eligible for finance, without going through bulky, long procedures like background checks, credit score evaluations and more. Normally, processing data happens with a combination of Python-based tools (therefore, machine learning-related technologies) included within a Java container, but this, of course, depends on how the company decided to set up its architecture.
In Simpler Terms
Although this may seem frighteningly complicated, the process is relatively simple to explain: since machine learning operates around variables, the tool (hypothetical) will elaborate the risk of approving any form of loan to a user by dividing the process into micro variables. These could be doing a credit check, cross-referencing proof of addresses, a tax code eventually. By dividing a bulky process into small tasks, then, the tool is able to (almost) instantly process them and therefore assess whether if the loan could be approved or rejected. By dealing with the enquiry almost instantly, the risk level of such a financial process is definitely lowered down.
The Market Value
When it comes to machine learning being applied to finance or any form of technology being applied to finance, really, it’s mandatory to analyse the market value in order to assess the power of that software/idea in such a busy and noisy market, in 2019. The fintech sector as a whole (and not just machine learning applied to risk management) has grown by over 25% in the past couple of years, according to Forbes. Within the property sector, in particular, it has been seen how automated pieces of software have been used to quickly resolve compulsory purchase order processes.
Currently, the usage of sole machine learning within fintech has amounted for over $1 billion in investments from 2015.
The power of fintech is no secret, but its applications within risk management for financial processes have been underestimated massively in the past couple of years. With this data in mind, it’s safe to say that machine learning and automated features will become an industry standard for risk management in finance before 2030.