A4Everyone Blog

More certainty for the consumer finance business amid COVID-19

“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.

Retail certainty in an uncertain time

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.

How data analytics helped Boston to save $5M and forced UPS to ban left turns

The students in the USA are eligible for school bus transportation if they live beyond a particular radius of the school. The yellow school buses are largely unchanged since their debut in 1939 even though their routes are getting more and more complex in the past 80 years. This is because of the number of students, schools, road systems and the system of rules.

When AI helps you choose a gift

Good retailers sell well, and best retailers offer their customers an unforgettable experience. The story of the 1800-Flowers.com online store is interesting because it traces the path that a Manhattan flower shop bought in the 1970s for $ 10,000 to a multimillion business that generated $ 1.2 bn. in sales just last year. The achievement goes through impressive AI innovation that has cemented its leadership position in the US online marketplace.

How Predictive Analytics Made Inventory Management Better

Not matter if a business is a production, retail or wholesale focused, the inventory is a crucial piece of its smooth running. It is a challenge that has to be faced properly. If not, efficiency wouldn’t be gained, resulting in not acceptable business performance. Inventory management executed as it should mean less cash locked in a stock, on time deliveries and at the end of the day – happy clients. As we all know, they do matter.

Predictive analytics is applicable as efficiency booster in many business processes and inventory management is no exclusion. Optimizing inventory is ensuring the right SKU is available in the right quantities, at the right time and at the right location. Such optimization is leading to stock levels reduction, hence transportation costs reduction and write-down cost reduction. Relying purely on data, predictive analytics is the perfect tool for addressing issues like this. Combine demand prediction with sales forecasting and you`ll know what, when and where.

5 AI Myths. Busted

Since AI has turned into a buzzword, many misperceptions created too many myths we`d like to share with you and bust together just like Adam Savage and Jamie Hyneman. Even though we are living right within the Secondary Information Age, the lack of understanding of what actually is AI and where it can bring the society we are all living in, it is a foundation of quite a few misconceptions. Some of them funny, other rather scary.

How credit scoring automation made FinTech lending possible

There was a time when the banks were the sole legitimate lending providers but the rapid increase of Internet coverage, smart devices penetration and credit scoring automation made possible to get a fast loan with just a few clicks on your phone. Welcome to the FinTech world built by much more than cryptocurrencies and ICOs. Credit scoring automation is the tool that enabled loan access to the underfinanced population. Matching credit scoring with machine learning, AI and automation, in general, made this process a viable business case.

Price Elasticity: A million-dollar retail question

A DIY retailer, for instance, has the potential to increase its sales revenue by 1 – 2% annually with price elasticity of demand application.

Meeting customers’ needs is essential for a retailer wellbeing. A retail business failing to do so will be simply pushed out of the market, soon or later. Some retailers rely on their gut feeling but as data analytics experts, we are perfectly familiar with Arthur Conan Doyle who once said, “It is a capital mistake to theorize before one has data”.

Why credit scoring as a service is the most viable option for consumer finance companies

Credit scoring is the process enabling lending businesses to determine how likely a lender will default its loan. This process is utilizing different data sources and less or more is designed around the business logic of the lending organization. In some markets like USA and UK, there are credit scoring bureaus assigning credit score derived from credit files and history of a particular person. In other markets, the credit scoring is up to the lending institutions, not matter banks or nonbank financial institutions like leasing companies, consumer finance companies, telecommunication companies, etc. Such businesses hold the credit risk and they need a solution like credit scoring. In this blog post, we explain why credit scoring as a service is the most viable option for lending businesses of different size and markets. Let us share with you the three main options in front of a company dealing with such kind of financial risk.