Maximize Value Across the Entire Customer Lifecycle
2022
What’s driving the agenda for fintechs and financial services in 2022? Pulse and Provenir surveyed 400 decision-makers in fintechs and financial services organizations globally to find out what they believe will be the biggest challenges, opportunities and trends of 2022 and how they plan to address them with data, AI and decisioning.
Data collection: October 13 December 21, 2021
Respondents: 400 Fintech decision-makers
The lack of data access, effective AI, and real-time decisioning are key challenges facing decision-makers today.
Most decision-makers struggle with their organization’s credit risk strategy because data is not easily accessible 74% , access to alternative data sources is limited 60% , and a centralized view of data across the customer lifecycle is not available 60% .
Which of the following do you consider to be the biggest challenge(s) with your organization’s credit risk strategy today?
Data is not easily accessible
74%
Limited access to alternative data sources
60%
Do not have centralized view of data across the customer lifecycle Unable to utilize machine learning / AI to improve model performance
60%
48%
Can’t make credit risk decisions in real time
43%
Models are difficult to deploy
39%
Too dependent on vendors to make changes
37%
23%
Models are not predictive
Do not have adequate internal resources
12%
Real-time credit risk decisioning is the #1 investment focus for 2022.
74% are planning to invest in a real-time credit risk decisioning platform in 2022. 69% plan to invest in AI-enabled credit decisioning and more than 50% plan to invest in digital wallet, financial inclusion and alternative data.
In which of the following do you plan to invest in 2022?
Planning to invest
Not planning to invest
Real-time credit risk decisioning platform
74%
26%
Digital wallet
71%
29%
AI-enabled credit decisioning
31.5%
68.5%
Products that enable financial inclusion
63%
37%
Embedded finance
55%
45%
Alternative data for credit risk
53%
47%
Buy now, pay later BNPL
46%
54%
SuperApps
41%
59%
AI and data integration are the two most important features when selecting an automated risk decisioning system.
Almost 50% of respondents plan to invest in an automated risk decisioning solution with alternative data capabilities in 2022.
Which of the following features does your automated risk decisioning system currently include / will be important in your selection?
Currently have feature/would be most important
Plan to invest in 2022
No plan to invest in 2022
Alternative Data
35%
48.5%
16.5%
Case Management
39%
41.5%
19.5%
Model language agnostic
42%
41%
17%
Real-time decisioning
52%
32%
16.5%
Business Intelligence
61%
31%
8%
Data Integration
65%
25% 10%
AI Predictive Analytics/Machine Learning)
64%
22% 14%
Fraud prevention is the biggest driver for investments in AI- enabled risk decisioning.
For 78% of respondents, fraud prevention is among the primary drivers of their investments in AI-enabled risk decisioning. More than half say their investments are driven by a desire to automate decisions across the credit lifecycle 58% and improve cost savings and operational efficiency 57% .
What are the primary drivers for any investments you’ve made/will make in AI enabled risk decisioning?
78%
58%
57%
51%
47%
42%
39%
37%
34%
Fraud prevention
Automate decisions across the credit lifecycle
Cost savings and operational efficiency
More competitive pricing
More accurate risk profiles
Improve customer experience
Improve financial performance
Grow market share with underbanked/ unbanked populations
Increase market differentiation
Only 21% of respondents begin to see returns from their AI projects within 120 days. 59% of respondents reported that they did not begin to see ROI from their AI implementations for 3 4 months.
How long after implementing AI (predictive analytics/ML did you begin to see positive ROI?
59%
121 150 days
18%
5% 0 60 days 16% 61 120 days
More than 150 days
2%
We have not implemented AI
1%
We have not seen positive ROI yet
Data integration is the biggest impediment to using alternative data.
70% of respondents list data integration challenges as one of the biggest barriers to using alternative data. 51% stated that alternative data is not accessible to their organization.
What are your biggest barriers to accessing/using alternative data?
70%
51%
46% 36%
Alternative data is not easily integrated into our current decisioning solution
Alternative data is not accessible to my organization
The data is too unstructured
Not convinced of the value
Improving fraud detection and prevention and serving the underbanked/unbanked are the top reasons respondents use alternative data in credit risk analysis.
65% of respondents say improving fraud detection and prevention is one of the primary reasons for using alternative data in credit risk analysis; 40% cite more accurate credit scoring. 51% use alternative data to better serve the underbanked/unbanked.
What are your primary reasons for using alternative data sources in credit risk analysis?
65%
43%
40%
Improve fraud detection and prevention
Reach a bigger audience
More accurate credit scoring
31%
51%
Our competitors are doing it
Serve the underbanked/ unbanked
26%
Alternative data is now available
11%
We don’t use alternative data
Age verification, bank account verification and KYC lead the pack for top types of data used in risk decisioning.
Age verification 71% , KYC 61% , and bank account verification and validation 61% are the top sources of alternative data used in risk decisioning by respondents.
Which of the following sources of alternative data do you currently include in your risk decisioning?
71%
Age Verification
Bank Account Verification & Validation
61%
KYC Know Your Customer
61%
Income Verification & Validation
48%
47%
Facial Biometrics
Document Verification & Validation
43%
43%
Employment Verification
37%
Open Banking
36%
Collections
36%
Identity & Verification
30%
KYB Know Your Business
29%
Fraud
27%
Affordability
3%
Social Media
Fraud prevention and operational efficiency are the two top priorities decision-makers plan to address with technology investments in 2022.
In 2022, decision-makers are looking to technology to help prevent fraud 70% , improve operational efficiency 60% , and increase their mobile presence 58% .
What challenges/opportunities do you plan to address via technology in 2022?
70%
60% 58%
50%
49%
44%
44%
42%
Fraud prevention
Improving operational efficiency
Increasing mobile presence
Acquiring new customers
Increasing regulatory compliance
Customer experience and retention
Risk mitigation
Increasing competition from new players
Confidence in credit model accuracy is low.
Only 18% of respondents feel their credit models are accurate at least 75% of the time.
How accurate do you believe your organization’s credit risk model (or models? is today?
76%
7% Not at all (less than half the time)
17% 1%
Somewhat 50% 75% of the time)
Mostly 76% 95% of the time)
Completely
Almost 80% of respondents cited low/no code as an important feature of automated risk decisioning systems.
78% of respondents cited low/no code UI as a feature they have or that would be most important when selecting an automated risk decisioning system and 19% plan to invest in 2022. 42% of respondents cited model language agnostic and 41% plan to invest in 2022.
Which of the following features does your automated risk decisioning system currently include / will be important in your selection?
Currently have feature/would be most important
Plan to invest in 2022
No plan to invest in 2022
78%
42% 41%
19%
17%
4%
Low/no code UI
Model language agnostic
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