AI | FinTech – Forget scorecards and turn your credit scoring into a money-making machine [VIDEO]

AI | FinTech - Forget scorecards and turn your credit scoring into a money-making machine [VIDEO]

WiredBrief are delighted to bring to you an exciting presentation from the second edition of AI Waves, which looks at Nextbanks AI driven FinTech credit scoring solution

About this event

Krzysztof Kogutkiewicz – CEO at NextBank discusses AI and Credit Scoring

Exciting developments in Artificial intelligence (AI) are explored by AI Waves during their second instalment: Forget scorecards and turn your credit scoring into a money-making machine, which looks at AIs application within FinTech.

This keynote focuses on NextBanks highly sophisticated credit scoring solution which helps solve traditional problems experienced in banking, relating to credit score transparency and maintenance. The use of AI within credit scoring has allowed large amounts of data to be used to determine specific actions / factors that affect credit user scores. These factors are then used to automatically create credit scoring models, which enable more proactive decisions to be taken by both banks and private users alike.

Anna Gawlikowska – AI Waves

Krzysztof Kogutkiewicz – CEO at NextBank

 Key Takeaways

  • The team’s algorithm predicted loan repayments, with a predictive accuracy of 97%.
  • Banks and private users can use NextBank’s solution to not just predict the likelihood of loan repayments, but to also highlight the specific factors that cause increases and decreases to credit score.
  • The implications of NextBank’s solution mean that loan pre – approvals can be automated by defining a cut off criteria that approves high probability applicants whilst referring riskier ones for manual review.
  • Banking teams can also use NextBank’s solution to pro-actively reach out to individuals with higher repayment predictive scores to offer lucrative loan terms before loan applications are made.

Machine learning Models used by NextBank:

  • Linear regression
  • LightGBM
  • XGboost

Result metrics used by NextBank.

  • ROC
  • F1 Score (Precision and Recall)

The following topics were covered:

  • What is Credit scoring 5:38
  • How does traditional Credit scoring work? 6:07
  • Credit scoring – What are the challenges? : Hard to interpret and reactive 7:16
  • What if credit scoring could return probability of return of a given loan they are applying for? Allows automating threshold acceptance. Only allow 80% success loan applications else manually review. 12:12
  • Show how a given factor affects a final result (ie credit history, private lending, demographics etc): 13:15
  • Can we create a system that can process 1000s of data points? 15:00
  • Can we create a pro active system that can recommend good burrowers?: 16:00
  • How was the AI system trained and what data sets were used?  18:00
  • What models were used? 20:33
  • Model goal? 22:15
  • Nextbank Credit Scoring solution and results 24:36
  • Questions: 29:00

Interested in learning more about AI / Machine learning Services?

Checkout: Miquido

Build world-class digital products with a team of design, development and strategy experts. All in one place.
About Geofrey Banzi, Legal Technologist, Big Four 16 Articles
Geofrey Banzi is a Legal Technologist at KPMG, co-organiser and co-founder of Legal Hackers MCR and the founder of WiredBrief, a leading tech platform that connects readers globally to the connected digital world. WiredBrief specifically focus on raising awareness of important tech-law concepts and issues, with the aim of creating greater awareness and understanding of technology and its potential to shape society for the better, as well as its portended risks which crucially need to be mitigated against. Geofrey is also the author of Regulating Driverless RTAs: A Concise Guide to the Driverless Future and Emerging Policy Issues in the UK and is a leading voice in the UKs rapidly growing Technology law scene. Specialisms and interest include: * Corporate, Competition and IP Law * Self driving cars and AI liability * Project management (Legal tech) * HighQ and cloud infrastructure * Data visualisation and UX system design * Document Automation (Contract Express)