Reimagining lending july 2018
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As the world continues to digitise it is commoditising every aspect of financial services whilst simultaneously presenting opportunities for adding value and differentiating. This presentation describes the challenge and a three step strategy for competing based on data, CX and starting with The Why
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Reimagining lending july 2018
- 3. Summary • Mortgages are the key line • Open Banking and Fintech are attacking hard • It’s a data play for scale and effectiveness • Defend the base with insight, action, reward and recognition
- 5. Disruption along the length of the value chain Hash Ching • Lead generation • Compete on price Lending Club • P2P • Easy access After Pay • Lay Buy in 2018 • Market Val $2bn
- 7. OPEN BANKING • Open Banking is the first implementation of Consumer Data Right • Response to the recommendations of the Productivity Commission’s Inquiry into Data Availability and Use. • The CDR will give customers the right to access their data in a machine-readable form. • Will drive the Big Switch
- 8. • Since 2014, 14 new banks have been approved in the UK, with another 20 entities in talks • August 2016 Tyro Payments was approved to become the first non-bank Authorised Deposit- taking Institution (ADI) in more than a decade • Sandbox and changing requirements 7/19/2018 8
- 9. Data Platform 9 “Artificial Intelligence is the new electricity. It’s transforming industry after industry, and financial services are particularly ripe for innovation.” – Andrew Ng, Chief Scientist at Baidu
- 10. AI in Lending • More Loans • Less default and switching • How? Dynamic Rule Set that learns • Predicated on big data • Can • Improve determinations of creditworthiness • Streamline the loan process • Improve the customer experience for borrowers
- 11. Introducing CAROL – Customers at risk of leaving Who is she? Customers enquiring about balance amounts, exit fees and requests for statements Home loan customers in the 18 to 24 months period after settlement Home loans that are over 3 years old Enquiries about fixed rates, balance enquiries 4 additional key insights ML will uncover more
- 12. The Metrics of Lending Product Line % Average Contribution Fees 2 Mortgage 39 Personal Loans 17 Credit Cards 42% Segment Run off 18-24% Average Home Loan length 3.45 years Mortgages is a key line to defend A customer who is contacted within 90 days of their loan settlement is 2.6 times less likely to leave that financial institution – take other products with them (deposits, etc.) 45% of customers who do not receive any contact from their current financial service provider refinance with another financial institution Less than 12% of customers leave their current financial service provider if they are contacted every six months by their current provider
- 13. Manual and inefficient Unable to process large document volumes Verification errors Resource constraints Costs money and client goodwill Higher cost per application Slow business, compliance, policy and risk change adaption Limitations to 3rd party verification services And more… Document verification for loan applications Further Information: Justin Gale justin.gale@sandstone.com.au Introducing DiVA 80% of the verification process can be automated
- 17. From Unbundled to Rebundled – Driven by Open banking
- 20. The Now of Digital Banking Moven – Compete on Saving Fidor – More Likes = better Interest Acorn - Roundups Atom Bank – No more Lists! • All Accounts In One Place • Digital Financial Advisor • Simplify Bill Payments • Competitive Credit Cards • Markets 3rd Party Loans
- 21. 7/19/2018 21
- 22. Defend the base staunchly – •Use AI to be data driven around out reach •Recognise and make it fun •Originate Online
Editor's Notes
- A very major part of our platform and story is our plan to build a very rich data set upon which to report the posture of our customers Day 1 will be standard DW – We will build our own reporting data model, by segment into which we shall pull the data and apply the rule set that will be managed by the compliance team. For this it will be hard to go past MSFT Azure Data Factory to give us the ETL, Scale, security, rules engine and analytics that we need to get to steady state. I have a day in Redmond with the Microsoft engineering team this Friday who I know well to get into this. That as a platform I am sure will be familiar to many Going forward, Machine Learning has enormous potential in Financial Services and for Fintech Bank particularly. As McKinseys note, “The enterprise winners will focus on micro verticals in promising industries.” Where we used to have to build data models and algorithms to map behaviours and trends manually, Deep learning allows us to do that on the fly and deeply. This will be our Ace ML can dramatically improve the accuracy, cost and speed of making and monitoring credit decisions. In a world where this is high volume and highly manual today this is very exciting for Fintech bank and our borrowers. Lenddo, for example, a Singapore-based software-as-a-service company which uses non-traditional data comprising social media and smartphone records to augment traditional data Uses advanced machine learning techniques to create dynamic predictive algorithms with over 12,000 variables to create their LenddoScore, increasing approval rates by 50% and decreasing default rates by 12%. This is a great example of what we can do at Fintechbank Here the answers are less mainstream in FS but increasingly well known. Google with TensorFlow, NVIDIA with their GU Cloud, Splunk, SAS as well as Microsoft and IBM Watson gives us tons to choose from and we’re very excited about bring AI, specifically Machine Learning to this sector to increase lending speed and accuracy
- Quadrant 1 The majority of financial institutions have automated their loan origination processes to include some form of digital data and document capture. But there’s often still a log jam where responsible lending processes require manual document verification for loan approval. Quadrant 2 Artificial Intelligence engines replace low value manual tasks Machine Learning means the system accuracy metrics continuously improve OCR capture is channel and format agnostic Rules Engine allows the bank to custom configure and amend responsible lending and verification criteria Application data amendments – Any manual input is done in the one screen Verification and authentication Interfaces – smart linking to ABN, ATO and benchmark lookups Automated messaging services