How are FinTechs using AI and machine learning to optimise decision making and support customers at a time of economic uncertainty?


FStech and Experian are conducting a brief survey to assess the ways in which FinTech companies are harnessing AI and machine learning to optimise decision making, drive innovation and support customers and clients through the cost-of-living crisis.

Please fill in this brief survey to help us build a picture of the key challenges, strategies and solutions for FinTechs as they look to use data, AI and new technologies to keep up with competitors in an era of digital transformation and innovation.


Questions
1. Which area of FinTech does your organisation specialise in? [Select one option]
Retail banking/account services
B2B/SME banking/account services
Retail lending/loans
B2B Lending/loans
Payments Service Provider (PSP)
Credit card provider
Buy Now Pay Later
WealthTech/Savings/Asset Management
Mortgages/high value transaction lending
InsurTech
Quote aggregation
Open Banking/data sharing
Blockchain/Digital Assets
2. Which of the following are your current priority areas for your business when it comes to making credit-related decisions? [Select all that apply]
Managing fraud/financial crime
Leveraging the insights in data to improve credit risk
Regulatory compliance
Improving credit strategies
Leveraging AI and ML
Improving affordability assessments
Improving automation/reducing manual intervention
3. Where would your organisation benefit most from using AI and ML? [select all that apply]
Automated decisioning
Credit risk
Analytics
KYC
Onboarding
Authentication
Payments
Regulatory reporting/compliance
4. What are the key drivers for rolling out automation, AI and machine learning in your organization? [Select top three]
Innovation and competitive advantage/growth
Compliance
Improving digital customer experiences
Straight through processing
Reducing costs through improved operations
Gaining insights into data and trends
Risk management
Value-added services
Enhancing scoring and modelling processes
5. To what extent does your organisation use AI and machine learning to automate decisions? [Select the most appropriate option]
We have implemented AI and machine learning for fully automated credit decisions
We have started rolling out AI and machine learning for automated credit decisions but there is some way to go
We have rolled out AI and machine learning for certain products but are still working on the more complex/higher risk use cases
We are looking to outsource AI and machine learning
We have a strategy in place to roll out AI and machine learning for credit decisions
We do not use AI and machine learning for credit decisions
Credit decisioning is a mostly manual process for our credit risk teams
6. What are the key barriers to automating credit decisioning in your organisation? [Select all that apply]
Lack of in house skills/training
Budget constraints
Finding appropriate partners
Security
Data availability
Disparate systems/complex architecture
Low conversion rates
7. How does your organisation operate credit risk decisioning? [Select all that apply]
We have built an in-house credit risk decisioning function
We have a mix of in-house automated and manual credit risk decision tools
We have a mix of in-house and third party credit risk decision tools
We use mainly third party credit risk decision tools
We have limited capacity for credit risk decisioning technology
8. Which data sources are you currently using to make decisions? [Select all that apply]
Archive data
Customer input data
Onboarding data
Open Banking data
Credit bureau data
Social media/contextual data
Salary data
Video calls/direct consultation
ID verification
9. What are the biggest data-related challenges for teams tasked with making credit risk decisions? [Select all that apply]
Lack of clarity on ID
Fraud risk
Lack of data to complete due diligence
Affordability checks
Confidence in credit risk models/accuracy of models
Lack of staff/resources
10.How do you project the cost-of-living crisis will impact your business over the coming year? [Select all that apply]
We are expecting to see higher default rates/delinquency
We are likely to scrap plans for BNPL products
We are likely to adapt decisioning to cope with added complexity
We are likely to need additional data and analytics for confidence across our market and portfolio
We are looking to implement model monitoring to ensure we are performing well
We are likely to offer fewer products
We are likely to pause product development
We are likely to develop products/methods that support struggling customers
We are likely to focus specifically on compliance and regulation
11. Which of the following are priorities for your company for enabling innovation and future growth? [Select all that apply]
Data access & insight
AI and machine learning
Product/service innovation
Market potential
Greater automation
Choosing the right technology partners
Please complete your details below.

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