Nigerian fintech startup, Kredete, has officially launched its lending platform to deepen accessibility to formal credit, financial education, and innovative financial solutions. The launch event, which took place on the 16th of April 2023, at the Civic Centre, Victoria Island, hosted tech specialists and entrepreneurs, who convened to discuss the future of lending in Africa and Kredete's commitment to bridging the significant credit gap and financial exclusion faced by Nigerians who lack access to formal credit through traditional means. Kredete aims to revolutionize the sphere of lending in Nigeria and beyond by leveraging AI-driven technology and strategic partnerships with financial institutions to create a complete lending infrastructure that enables lenders to be more efficient and cost-effective while empowering users to access credit products suited to their needs. With AI, the pre-approval odds for users enhance by providing tailored loan options based on their credit history and financial profile, while for lenders, it becomes easier to assess risks, reduce non-performing loans, and make informed lending decisions. Kredete is also passionate about strengthening financial literacy in Nigeria and will achieve this by offering free credit scores, reports, and monitoring tools to help users understand their credit standing and make informed financial decisions.

ClearScore is searching for Data Scientist. This is an amazing opportunity to engage with a rich data set on 15 million users. We have captured every interaction the users have had with our high touch website and native apps, their fully monthly credit files and growing access to insurance, bank account and mobile data as well. Ordinarily a data set this vast would be buried in legacy systems, however ClearScore is only 6 years old and operates a strict'zero legacy' approach that means the data is held in an autoscaling AWS redshift environment. This is an exciting opportunity to work within a fast-paced, rapidly growing team of talented data scientists, helping to build revolutionary experiences that will help millions of users manage and better understand their finances.

When a financial institution declines an application for credit, an adverse action (AA) is said to occur. The applicant is then entitled to an explanation for the negative decision. This paper focuses on credit decisions based on a predictive model for probability of default and proposes a methodology for AA explanation. The problem involves identifying the important predictors responsible for the negative decision and is straightforward when the underlying model is additive. However, it becomes non-trivial even for linear models with interactions. We consider models with low-order interactions and develop a simple and intuitive approach based on first principles. We then show how the methodology generalizes to the well-known Shapely decomposition and the recently proposed concept of Baseline Shapley (B-Shap). Unlike other Shapley techniques in the literature for local interpretability of machine learning results, B-Shap is computationally tractable since it involves just function evaluations. An illustrative case study is used to demonstrate the usefulness of the method.

Cleo was created to improve your financial health. Already, she's helped over 3 million people improve their relationship with money through simplicity and a sense of humour. She's an interface for the 99% – an AI assistant defining a new category, one that goes beyond saving up to actually changing how we feel about our finances. Through chat, Cleo hits you with ridiculously personal insights into your spending, while suggesting personalised financial products that increase your ability to save. That means we're meeting our users where they are and building the type of relationship they expect.