“Only when the tide goes out do you discover who has been swimming naked.”
Warren Buffett
Those who act fast and adapt to turmoil will survive and succeed. In the context of the Coronavirus pandemic and the lending business, this means that financial institutions should have instruments to continuously analyze on a permanent basis what is happening and react immediately if needed.
As experts in credit scoring utilizing artificial intelligence (AI) and machine learning (ML), we have prepared this publication to offer an in-depth understanding of the problems the consumer finance business is currently facing. We focus on counteraction mechanisms, emphasizing the potential of automated decision-making in general. The dramatic change in people’s lives, the social distancing measures and the heterogeneous impact of the crisis on particular segments of the population, especially employment, as well as the need for additional financing led to a shock for the consumer finance business. As a result, some businesses have drastically reduced their operations and switched their credit scoring processes to manual loan approvals. This significantly reduced the speed and efficiency of their lending process as a whole. Some companies even chose to shut down part of their business abroad due to fears of significant deterioration of their loan portfolio and the negative effect of the imposed legal measures on debt repayment and collateral liquidity.
The COVID-19 pandemic is unprecedented in the recent days cataclysm. Countries from all over the world found themselves in a wholly new and, in most cases, unexpected situations. It applies to not just governments and businesses but also consumers.
The retail sector has been shaken by the dramatic change that forced some retailers to close their outlets, others to shift to e-commerce, while other businesses are facing drastic demand surge. When consumer behavior is rapidly changing, the right retailers’ response is essential, and their future might be at stake.
Just think of it! Without credit and lending businesses, we would not be able to buy a house or appliances when we need them. Business would not be able to grow, expand and innovate at the desired pace. Credit is the fuel of any country` economy and this is not a secret. But what is fueling credit and lending businesses? How do they decide if you are eligible for a particular loan or not?
For just a decade, the telco sector is nothing like what it was like. Telecommunication companies faced transforming changes and data is the centerpiece. First, mobile telecom companies shifted their focus from voice vendors to data carriers. Fixed services became obsolete and experience downfall. Competition became more fierce than ever because a market maturity means a limited amount of end users even though IoT are percepted as a potential booster. So, such situation should be examined more as an opportunity than like a dangerous threat.
Big data and predictive analytics have a lot of applications, including sales forecasting, marketing and strategy optimization, machine maintenance, even sports and design. Police and security experts already know that big data and predictive analytics might be extremely helpful in fighting and preventing crimes. We are going to explain how they do it.
Believe it or not, big data is the new gold. Poured into the fuel tank of the automotive industry, it is transforming itself into growth booster, creating new services and users` benefits. The core focus of big data and cars is not just autonomous vehicles, it is about humans and how they use their cars on an amazingly granular level.
Strategy optimization (SO) is a key process, which provides a substantial growth for retail and utility companies.
Generally the application of SO is directed to campaign management – helping the organizations to interact in a better way with their customers. The most popular optimization goals are increase of the revenue, popularity or customer loyalty, reducing the taken risk and so on. The multi-strategy optimization is also an option, where a balance between contradictory objectives is investigated, like the fine tuning between profit and loss.
Predictive analytics is full of tools and approaches enabling it to reveal key insights in almost any area. We already discussed the impact of Data Analytics in HR and we are delivering further.
Recent blog post by Toshi Takegushi, part of MathWorks team reveal in an interesting and comprehensive way how a predictive analytics model can be triggered on job-related data sets for scoring which employee is planning to quit its position. He relied on machine learning algorithms for predicting future events by utilizing historical data.
In this blog post, our chief scientist Alexander Efremov PhD. is discussing the application of direct and recurrent artificialneural networks (ANN) in some methodologies for credit risk models.
Inputs of ANN could be the available individuals’ characteristics, like age, income, marital status, credit bureau data, etc. The outputs are the probability the applicants to have a good performance as loan-holders, the individuals’ response to some actions, etc.
Google just surprised the data savvy community by withdrawing the limitation of 5 reports created within the free trial in their Data Studio product. The change was announced by Nick Mihailovski, product manager for Google Data Studio, in a recent blog post and via the official Google Analytics Twitter account.
We removed Data Studio limits based on your feedback. We’d love to hear what new integrations you’d like to see: https://t.co/wRgjTfARYe
