Big Data, Risk Analytics and AIin Finance and Insurance

Tze Leung Lai

Stanford University

June, 2018

Outline

Big Data Analytics and AI for the Insurance IndustryBig data in insurance industryUsage-based insurance

Risk Analytics in: Banking, SME and P2P LendingBank’s risk managementSmall and Medium-Sized Enterprise (SME)Peer to Peer (P2P) Lending

Blockchain and RegTech

Big data in insurance industry

I The amount and variety of data available to insurance companiestoday provide a wealth of new opportunities to increase revenue,control costs, and counter competitive threats.

I Huge volumes of data related to demographics, psychographics,claims trends, and product- related information are starting toenable better risk assessment and management, new productstrategies and more efficient claims processing.

I Some of the use cases for Big Data analytics in insurance include:

I Risk avoidanceI Product personalizationI Cross selling and upsellingI Fraud detectionI Catastrophe planningI Customer needs analysis

Risk avoidance

I Today, relationships between insurance agents and their customersand communities are decentralized and virtual. Insurers can,however, access a myriad of new sources of data and buildstatistical models to better understand and quantify risk.

I These Big Data analytical applications include behavioral modelsbased on customer profile data compiled over time cross-referencedwith other data that is relevant to specific types of products. Forexample, an insurer could assess the risks inherent in insuring realestate by analyzing satellite data of properties, weather patterns,and regional employment statistics.

Product personalization

I The ability to offer customers the policies they need at the mostcompetitive premiums is a big advantage for insurers. This is moreof a challenge today, when contact with customers is mainly onlineor over the phone instead of in person.

I Scoring models of customer behavior based on demographics,account information, collection performance, driving records, healthinformation, and other data can aid insurers in tailoring productsand premiums for individual customers based on their needs and riskfactors.

I Some insurers (in usage-based insurance) have begun collectingdata from sensors in their customers cars that record average milesdriven, average speed, time of day most driving occurs, and howsharply a person brakes.

I This data is compared with other aggregate data, actuarial data,and policy and profile data to determine the best rate for eachdriver based on their habits, history, and degree of risk.

Cross selling and upselling

I Collecting and gathering data across multiple channels, including

I Web site click stream data,I social media activities,I account information,

and other sources can help insurers suggest additional products tocustomers that match their needs and budgets.

I This type of application can also look at customer habits to assessrisks and suggest alteration of habits to reduce risks.

Fraud detection

I Insurance providers are looking beyond algorithmic fraud detectiontechniques that are claim-centric, to ones that are personcentric.

I These techniques focus on analyzing beneficiary behavior acrossclaims, providers, and other sources of information (e.g. how manysimilar claims were submitted by the same individual, reported bythe same individual), and extend to data sources beyond the firewallto analytics based on external information (e.g. cohort analysis -using a persons social graph to look for similar activities amongconnected individuals), and considering networks of people ratherthan just individuals.

I Collecting data on behaviors from online channels and automatedsystems help determine the potential for and existence of fraud.

I These activities can help create new models to identify patterns ofboth normal and suspect behavior that can be used to combat theincreasingly sophisticated perpetration of insurance fraud.

Catastrophe planning

I Being proactive instead of reactive when extreme weather ispredicted or during or after its occurrence can in some cases lessenthe extent of claims and accelerate responses by insurers.

I In the past, this type of analysis was done through statistical modelsat headquarters but with the ability to gather data directly fromcustomers and other sources in real-time, more actionableinformation can be gathered and acted upon.

I USGS researchers found that people Tweeting about actualearthquakes kept their Tweets really short, even just to ask,earthquake? Concluding that people who are experiencingearthquakes arent very chatty, they started filtering out Tweets withmore than seven words.

I They also recognized that people sharing links or the size of theearthquake were significantly less likely to be offering firsthandreports, so they filtered out any Tweets sharing a link or a number.Ultimately, this filtered stream proved to be very significant atdetermining when earthquakes occurred globally.

Customer needs analysis

I Automating the discussion between prospects and advisors aboutcomplex insurance products such as life and annuity, based on acustomers desires and resources can enhance the sales process.

I These applications based on business rules go beyond simpledecision trees and algorithms to provide faster and more dependableinformation and options as part of the sales dialogue.

Others

I Other Big Data analytics applications in insurance include

I loyalty management,I advertising and campaign management,I agent analysis,I customer value management, andI customer sentiment analysis.

I These applications can enhance marketing, branding, sales, andoperations with business insights that lead to informed actions.

Examples

I MetLife is using Big Data applications to look at hundreds ofterabytes of data for patterns to gauge how well the company isdoing on minimizing risk, understanding how various products areperforming, and what the trends are.

I Travelers is using Big Data applications to rationalize product linesfrom new acquisitions and to understand the risks from globalgeopolitical developments.

I Progressive Insurance and Capital One are conducting experimentsto segment their customers using Big Data and to tailor productsand special offers based on these customer profiles.

Examples

I In China, Zhongan Insurance has been developing the Shipment FeeInsurance with Alibaba, which covers a specific amount of theshipment fee, like 9 RMB, if a customer wants to ship back theproduct he purchased on Alibabas online market Taobao.

I Previously, the insurance premium is set using rule of thumb, like0.5RMB without precise definition. Applying Big Data analysis, theinsurance company is trying to develop models using data like therate of return of goods, characteristics of the buyers, etc.

Usage-based insurance (UBI)

I Also known as pay as you drive (PAYD) and pay how you drive(PHYD) and mile-based auto insurance is a type of vehicleinsurance whereby the costs are dependent upon type of vehicleused, measured against time, distance, behavior and place.

I The simplest form of usage-based insurance bases the insurancecosts simply on the number of miles driven.

I However, the general concept of PAYD includes any scheme wherethe insurance costs may depend not just on how much you drive buthow, where, and when one drives.

Pay as you drive (PAYD)

Pay as you drive (PAYD) means that the insurance premium is calculateddynamically, typically according to the amount driven. There are threetypes of usage-based insurance:

I Coverage is based on the odometer reading of the vehicle.

I Coverage is based on mileage aggregated from GPS data, or thenumber of minutes the vehicle is being used as recorded by avehicle-independent module transmitting data via cellphone or RF(radio frequency connector)technology.

I Coverage is based on other data collected from the vehicle,including speed and time-of-day information, historic riskiness of theroad, driving actions in addition to distance or time travelled.

The formula can be a simple function of the number of miles driven, orcan vary according to the type of driving or the identity of the driver.Once the basic scheme is in place, it is possible to add further details,such as an extra risk premium if someone drives too long without a break,uses their mobile phone while driving, or travels at an excessive speed.

Telematic usage-based insurance

I The latter two types, in which vehicle information is automaticallytransmitted to the system

I It provides a much more immediate feedback loop to the driver, bychanging the cost of insurance dynamically with a change of risk.This means drivers have a stronger incentive to adopt saferpractices.

I For example, if a commuter switches to public transport or toworking at home, this immediately reduces the risk of rush houraccidents. With usage-based insurance, this reduction would beimmediately reflected in the cost of car insurance for that month.

Measurement using smartphones

The smartphone as measurement probe for insurance telematics has beensurveyed. Benefits to drivers include:

I Reduced accident frequency and severity

I Faster emergency response time following an accident

I Improved tracking to recover stolen vehicles

I Greater accuracy in establishing fault when settling claims

I Reduced driving, pollution, traffic congestion and energyconsumption

UBI in US and Canada

I Most of the top 20 auto insurers in the U.S. have implemented orare developing UBI products.

I The expansion in Canada may be even more dramatic. In 2013,there were only small pilot UBI products in Canada before the massmarket launch of Ajusto by Desjardins in Ontario and Quebec. Justover a year later, more than half of the top 10 Canadian insurershave either launched or are developing UBI products.

DriveAbility

Towers Watson has provided the DriveAbility service for UBI. DriveAbilityincludes consulting guidance by industry leaders, telematics devices andservices, hosted data cleansing, expert analytics and UBI risk scoring.

I Pooled data: With access to the industrys only pooled databasethat merges driver behavior with actual loss costs, insurers canimmediately implement our predictive DriveAbility score withconfidence.

I Predictive score: Our driving score is three times more predictivethan existing rating characteristics, and future scores will onlyimprove on this.

I Hosted infrastructure: We collect, scrub, link, store and analyzedata for you, saving you time and money.

I Project management: Pre-built tools for planning andimplementation will increase your speed to market.

Challenges: Privacy and cost

I Privacy concerns

I Legislation requiring disclosure of tracking practices anddevices has been enacted by some states in US.

I The data can be collected is limited.I Thanks to the increasing usages of mainstream technology

devices (such as smartphones, tablets, and GPS devices) andsocial media networks (such as Facebook and My Space),acceptance of information sharing is growing.

I Costly

I Collecting and sensitizing driving data need costly technology.I Still much uncertainty about the selection and analysis of the

collected driving data. It is also sometimes unclear how tointegrate the data into existing or new pricing models tomaintain profitability.

I Putting lower-risk drivers into UBI programs that offer lowerpremium could lower overall insurer profitability.

Challenges: regulatory requirements

I Regulatory requirements management

I Many states require insures to obtain approval for the use ofnew rating plans. Rate filings usually must include statisticaldata that supports the proposed new rating structure.

I Although there are general studies demonstrating the linkbetween mileage and risk, individual driving data and UBI planspecifics are considered proprietary information of the insurer.This can make it difficult for an insurer who does not havepast UBI experience.

I Other requirements that could prevent certain UBI programsinclude the need for continuous insurance coverage, upfrontstatement of premium charge, set expiration date, andguaranteed renewability.

I However, it should be noted that a Georgia Institute ofTechnology survey of state insurance regulations (2002) foundthat the majority of states had no regulatory restrictions thatwould prevent PAYD programs from being implemented.

Bank retail loans and FICO score

I Bank retail loans consist of residential mortgages, credit cards, carloans, personal unsecured loans and other consumer loans. Toapprove a loan, the bank evaluates the borrowers income, net worthas well as his/her FICO score.

I The FICO score was first introduced in 1989 by FICO, then calledFair, Isaac, and Company. The FICO model is used by the vastmajority of banks and credit grantors, and is based on consumercredit files of the three national credit bureaus: Experian, Equifax,and TransUnion.

I A consumers credit file has the data each time he/she applies for aloan or a credit card plus his/her payment history. Because aconsumer’s credit file may contain different information at each ofthe bureaus, FICO scores can vary depending on which bureauprovides the information to FICO to generate the score.

I The large number of consumers makes it as a big data problem.

Bank retail loans and FICO scoreCredit scores are designed to measure the risk of default by taking intoaccount various factors in a person’s financial history. Although the exactformulas for calculating credit scores are secret, FICO has disclosed thefollowing components:

I 35%: payment history:I This is best described as the presence or lack of derogatory

information.I Bankruptcy, liens, judgments, settlements, charge offs,

repossessions, foreclosures, and late payments can cause aFICO score to drop.

I 30%: debt burden:I considers a number of debt specific measurements.I According to FICO there are some six different metrics in the

debt category including the debt to limit ratio, number ofaccounts with balances, amount owed across different types ofaccounts, and the amount paid down on installment loans.

I 15%: length of credit history aka Time in File:I As a credit history ages it can have a positive impact on its

FICO score.I There are two metrics in this category: the average age of the

accounts on your report and the age of the oldest account.

Bank retail loans and FICO score

I 10%: Types of credit used (installment, revolving, consumerfinance, mortgage):

I Consumers can benefit by having a history of managingdifferent types of credit.

I 10%: recent searches for credit:

I Hard credit inquiries, which occur when consumers apply for acredit card or loan (revolving or otherwise), can hurt scores,especially if done in great numbers.

I Individuals who are ”rate shopping” for a mortgage, auto loan,or student loan over a short period (two weeks or 45 days,depending on the generation of FICO score used) will likely notexperience a meaningful decrease in their scores as a result ofthese types of inquiries.

I It is because the FICO scoring model considers all of thosetypes of hard inquiries that occur within 14 or 45 days of eachother as only one. Further, mortgage, auto, and student loaninquiries do not count at all in your FICO score if they are lessthan 30 days old.

Bank retail loans and FICO score

I While all credit inquiries are recorded and displayed on personalcredit reports for two years they have no effect after the first yearbecause FICO’s scoring system ignores them after 12 months.

I Credit inquiries that were made by the consumer (such as pulling acredit report for personal use), by an employer (for employeeverification), or by companies initiating pre-screened offers of creditor insurance do not have any impact on a credit score:

I these are called “soft inquiries” or “soft pulls”, and do notappear on a credit report used by lenders, only on personalreports. Soft inquiries are not considered by credit scoringsystems.

Bank retail loans and FICO score

I Getting a higher credit limit can help a consumers credit score. Thehigher the credit limit on the credit card, the lower the utilizationratio average for all of your credit card accounts.

I The utilization ratio is the amount owed divided by theamount extended by the creditor and the lower it is the higherFICO rating, in general.

I So if a consumer has one credit card with a used balance of $500and a limit of $1,000 as well as another with a used balance of $700and $2,000 limit; the average ratio is 40 percent ($1,200 total useddivided by $3,000 total limits). If the first credit card companyraises the limit to $2,000; the ratio lowers to 30 percent; whichcould boost the FICO rating.

I Other special factors:

I Any money owed because of a court judgment, tax lien, etc.,carries an additional negative penalty, especially when recent.

I Having one or more newly opened consumer finance creditaccounts may lower the FICO score.

Types FICO score

I There are several types of FICO credit score: classic or generic,bankcard, personal finance, mortgage, installment loan, auto loan,and NextGen score.

I The generic or classic FICO score is between 300 and 850, and 37%of people had between 750 and 850 in 2013.

I According to FICO, the median classic FICO score in 2006 was 723,and 711 in 2011. The U.S. median classic FICO score 8 was 713 in2014. The FICO bankcard score and FICO auto score are between250 and 900. The FICO mortgage score is between 300 and 850.Higher scores indicate lower credit risk.

Classic FICO score

I Each individual actually has more than 49 credit scores for the FICOscoring model because each of three national credit bureaus,Equifax, Experian and TransUnion, has its own database.

I Data about an individual consumer can vary from bureau to bureau.

I Different FICO scores’ names at each of the different creditreporting agencies: Equifax (BEACON), TransUnion (FICORisk Score, Classic) and Experian (Experian/FICO RiskModel).

I Four active generations of FICO scores: 1998 (FICO 98), 2004(FICO 04), 2008 (FICO 8), and 2014 (FICO 9)

I Consumers can buy their classic FICO Score 8 for Equifax,TransUnion, and Experian from the FICO website (myFICO). Othertypes of FICO scores cannot be obtained by individuals, only bylenders. Some credit cards offer to include the customers FICOscore in the monthly bills.

NextGen Risk Score

I The NextGen Score is a scoring model designed by the FICOcompany for assessing consumer credit risk. This score wasintroduced in 2001, and in 2003 the second generation of NextGenwas released.

I Each of the major credit agencies markets this score generated withtheir data differently:

I Experian: FICO Advanced Risk ScoreI Equifax: PinnacleI TransUnion: FICO Risk Score NextGen ( formerly Precision )

I Prior to the introduction of NextGen, their FICO scores weremarketed under different names:

I Experian: FICO Risk ModelI Equifax: BEACONI TransUnion: FICO Risk Score, Classic (formerly EMPIRICA)

VantageScore

I In 2006, to try to win business from FICO, the three majorcredit-reporting agencies introduced VantageScore.

I According to court documents filed in the FICO v. VantageScorefederal lawsuit the VantageScore market share was less than 6% in2006.

I The VantageScore score methodology initially produced a scorerange from 501-990, but VantageScore 3.0 adopted the score rangeof 300-850 in 2013. Consumers can get free VantageScores fromfree credit report websites.

Other Credit Score

I Many lenders have their own credit score models compiled from theapplication data. These lenders often have internal data notreported or used by the three credit bureaus. To obtain updatedcredit scores from the credit bureaus, each lender needs to reporttheir customer’s payment performance to the three credit bureaus.

I As a result of the FACT Act (Fair and Accurate Credit TransactionsAct), each legal U.S. resident is entitled to a free copy of his or hercredit report from each credit reporting agency once every twelvemonths.

I The law requires all three agencies to provide reports: Equifax,Experian, and Transunion. These credit reports do not containcredit scores from any of the three agencies. The three creditbureaus run Annualcreditreport.com, where users can get their freecredit reports.

Non-traditional uses of credit scores

I Credit scores are often used in determining prices for auto andhomeowner’s insurance. Insurance companies use them to rate theinsurance risk of potential customers.

I Studies indicate that the majority of those who are insured payless in insurance through the use of scores. These studies pointout that people with higher scores have fewer claims.

I In 2009, TransUnion representatives testified before the Connecticutlegislature about their practice of marketing credit score reports toemployers for use in the hiring process.

I Legislators in at least twelve states introduced bills, and threestates have passed laws, to limit the use of credit check duringthe hiring process.

CriticismCredit scores are widely used because they are inexpensive and largelyreliable, but they do have their failings.

I Easily gamed

I Because a significant portion of the FICO score is determinedby the ratio of credit used to credit available on credit cardaccounts, one way to increase the score is to increase thecredit limits on one’s credit card accounts.

I Not a good predictor of risk

I According to a Fitch study, the accuracy of FICO in predictingdelinquency has diminished in the past few years. In 2001there was an average 31-point difference in the FICO scorebetween borrowers who had defaulted and those who paid ontime. By 2006 the difference was only 10 points.

I Some banks have reduced their reliance on FICO scoring. Forexample, Golden West Financial (which merged with WachoviaBank in 2006) abandoned FICO scores for a more costlyanalysis of a potential borrower’s assets and employmentbefore giving a loan.

Criticism

I Use in employment decisions

I The use of credit reports for employment screening is allowedin all states, although some have passed legislation limiting thepractice to only certain positions.

I Eric Rosenberg, director of state government relations forTransUnion, has stated that there is no research that showsany statistical correlation between what’s in somebody’s creditreport and their job performance or their likelihood to commitfraud.

SME in European Union

The European definition of SME:

I Micro: up to 10 employees

I Small: 11 to 50 employees

I Medium: 51 to 250 employees

I have an annual sales not exceeding 50 million euro, and/or

I total assets not exceeding 43 million euro.

In 2009 in EU, 92.2% business are Micro, 6.5% are Small, 1.1% areMedium and 0.2% are Large

SME in the US

I Census Bureau data indicates that in 2011

I there were 5.68 million employer firms in the United StatesI 99.7% have fewer than 500 workersI 89.8% have less than 20 workers

I Add in 22.7 million of nonemployer firms in 2012

I 99.9% have fewer than 500 workersI 98% have less than 20 workers

I They produced 46 percent of the private nonfarm GDP in 2008 andaccounted for 63 percent of the net new jobs created in the US.

SME Statistics

I 51.6% of businesses were operated primarily from someones home.

I 23.8% of employer firms operated out of a home.

I 62.9% of non-employer businesses were home-based.

I About 28% of firms were family-owned. These family-owned firmsaccounted for 42% of all firms receipts.

I Business owners were well-educated: 50.8% of owners of respondentfirms had a college degree.

I And 13.6% of business owners were foreign born.

SME Default Models

I Edward Altman and Gabriele Sabato published a paper on SMEoneyear default prediction model in November 2006. The model wasdeveloped using the logistic regression for companies with annualsale less than $65 million. They pulled these companies financialdata from 1994 to 2002 from WRDS COMPUSTAT database.

I In the data, there were 120 defaults (with no missing data) duringthis period for the SMEs.

I A Moody’s 2004 study about the small and medium sized firms inthe US showed that the average default rate is 6%.

I To maintain the overall average expected default rate at 6%, 1,890other non-defaults were selected for their study.

Peer to Peer (P2P) Lending

I Wikipedia describes P2P lending as:

I the practice of lending money to unrelated individuals, or“peers”, without going through a traditional financialintermediary such as a bank or other traditional financialinstitution.

I Most P2P loans are unsecured personal loans. The interest rates are setby P2P companies based on the borrower’s credit. Borrowers with ahigher default risk are assigned higher rates. P2P investors can reducetheir credit risk (i.e., borrowers not paying back the loan) by choosingwhich borrowers to lend to. P2P lenders can further reduce their principalloss risk by diversifying their investments among different borrowers.Because P2P lenders can choose their borrowers, the P2P loans arelegally different from deposits in financial institutions. The P2P lendersinvestment in the P2P loan is not protected by any governmentguarantee. Even the bankruptcy of the P2P company that facilitate theloan may also put a lender’s investment at risk.

I The lending intermediaries are for-profit businesses; they generate revenueby collecting a onetime fee on funded loans from borrowers and by takinga loan servicing fee from investors (either a fixed amount annually or apercentage of the loan amount).

Most P2P companies provide the following services:

I Providing on-line investment platform to enable borrowers to attractlenders, and investors to identify and purchase loans that meet theirinvestment criteria

I Developing credit models for loan approvals and pricing

I Verifying borrower identity, bank account, employment and income

I Checking borrower credit history and filtering out the unqualifiedborrowers

I Processing payments from borrowers and sending payments tolenders who invested in the loan

I Servicing loans by providing customer service to borrowers andattempting to collect payments from borrowers who are delinquentor in default

I Legal compliance and reporting

I Through their marketing efforts, finding new lenders and borrowers

I Because many P2P services are automated, the intermediarycompanies can operate with lower overhead and can provide theservice more cheaply than traditional financial institutions.

I P2P borrowers may be able to obtain money at lower interest ratesand P2P lenders may be able to earn higher returns. Compared tostock markets, peer-to-peer lending tends to have both lowervolatility and less liquidity.

I Because P2P loans are not secured, they are likely to have muchhigher loss rates than the secured consumer loans such as first andsecond residential mortgages and auto loans.

I P2P loans are most comparable to credit card loans, and hencemuch higher interest rates comparable to credit cards.

I However, credit card loans are short-term (one to three months)while P2P loans have 3 or 5-year terms. It is reasonable to expectthat the P2P loans will have much higher default rates than thesecured consumer loans, or credit card loans.

Managing Catastrophic Losses is Imperative to Credit Risk ControlEmphasis is not often low loss,and in a one-off catastrophiclosses

I How much is the loss?

I How often?

I Why did it happen?

How to prevent similar loss in thefuture

I Hedge

I Portfolio sale

The ultimate question is whetherthere is enough capital to absorbthe massive loss

The key to these questions lies in understanding the credit lossdistributions

Typical credit portfolio loss distributions

I Tail risk refers to the end ofthe loss distribution

I The risk of catastrophicloss, usually from theunimaginable time , alsoknown as “Black Swan”

I estimate the amount ofcapital required to survive acatastrophic event

Control Tail Risk in Credit PortfolioNumerous causes of tail risk:

I Downward credit cycle (e.g. in 2007 the overall decline in UShousing prices )

I The system of internal financial system risks (such as theaccumulation of 2008 year of complex structured credit products)

I Economic slowdown (China 1993 banking crisis)

I Geo-political events (the oil crisis of the 1970s)

I Tail risk is often difficult to predict, difficult to measure and evenmore difficult to manage

I Common symptoms: Stealth leverage expanded credit, over relaxedstate of mind, lax lending standards

I The signs are easy to see for the experienced risk control officer, butnot easy to quantify

I Systematic and systemic causes which can not be dispersed

I Reliable hedge management ... but often difficult to convincemanagement to spend money

I Reliable regular portfolio rebalancing

US Example: Prosper Marketplace

I Based in San Francisco, California, Prosper Marketplace is the first(Feb 5, 2006) P2P lending company in the US, with more than 2.2million members and over $5 billion in funded loans. Borrowersrequest personal loans on Prosper website, and investors (individualor institution) can fund anywhere from $2,000 to $35,000 per loanrequest. In addition to credit scores, ratings, and histories, investorscan consider borrowers’ personal loan descriptions, endorsementsfrom friends, and community affiliations. Prosper handles theservicing of the loan and collects and distributes borrower paymentsand interest back to the loan investors.

I Prosper verifies borrowers’ identities and select personal data beforefunding loans and manages all stages of loan servicing. Theseunsecured personal loans are fully amortized over a period of threeor five years, with no prepayment penalties. Prosper generatesrevenue by collecting a one-time fee ranging from 1% to 5% onfunded loans from borrowers and assessing a 1% annual loanservicing fee based on the original loan amount to investors.

US Example: Lending Club

Lending Club: Overview

I Investors can search and browse the loan listings on Lending Clubwebsite, and select loans that they want to invest in based on theinformation supplied about the borrower, amount of loan, loan grade, andloan purpose. The loans can only be chosen at the interest rates assignedby Lending Club but investors can decide how much to fund eachborrower, with the minimum investment of $25 per note.

I Investors make money from interest. Rates vary from 5.32% to 28.49%,depending on the credit grade assigned to the loan. Lending Club makesmoney by charging borrowers an origination fee and investors a servicefee. The size of the origination fee depends on the credit grade andranges to be 1.1%-5.0% of the loan amount. The size of the service fee is1% on all amounts the borrower pays. The company facilitates interestrates that are better for lenders and borrowers than they would receivefrom most banks. It has averaged between a six and nine percent returnto investors between its founding and 2013. However, because lenders aremaking personal loans to individuals on the site, their gains are taxable aspersonal income instead of investment income. Therefore, income fromLending Club loans may be taxed at a higher rate than investments thatare taxed at the capital gains rate.

Lending Club: OverviewI Lending Club enables borrowers to create loan listings on its website by

supplying details about themselves and the loans that they would like torequest.

I All loans are unsecured personal loans and can be between $500 to$35,000. On the basis of the borrower’s credit score, credit history,desired loan amount and the borrower’s debt-to-income ratio, LendingClub determines whether the borrower is credit worthy and assigns to itsapproved loans a credit grade that determines payable interest rate andfees.

I The standard loan period is three years; a five-year period is available at ahigher interest rate and additional fees. About 29% of the 650,000 loanshave the 5-year term.

I The loans are fully amortizing, and can be repaid at any time withoutpenalty. Among the “Fully Paid” borrowers, about 40% paid off theirloans during the first year, 30% the second year. Only 16% of the “FullyPaid” borrowers wait until the end of the term to pay off their loans.

I From the investors perspective, the P2P loans have both the prepaymentrisk and default risk. While the bad credit borrowers default, the goodones prepay their loans. The double option risk will likely to make theP2P loans unprofitable from an option adjusted basis.

Default Rate Comparison: Prosper vs. Lending Club

China’s credit cycle in declining currently while the tail riskis rising

I Macroeconomic weakness

I GDP Growth: an average of 9.77 percent for 34 years (1979-2014)– 2012: 7.8%; 2013: 7.7%; 2014: 7.4%

I Structural and cyclical factorsI Soft landing is most likely that no large-scale real estate, banking

system collapse

I Supply of credit crunch, but money supply is still relax

I Financing exist , but will tightenI Bad debts gradually revealed, but will be capitalized

I Chinese P2P negative press coverage

I Despite the financial / banking system is relatively stable, P2Pindustry systemic crash risk cannot be ignored

I Survivor is king

Conclusions

I Big Data: High Volume and Multi-Dimensional Data; Precise Modeling

I Insurance Industry: Risk Avoidance; Product Personalization;Cross-Selling and Upsetting; Fraud Detection; Catastrophe Planning;Customer Needs Analysis

I Bank, SME and P2P Lending: Credit Scores; Default Models; Tail RiskControlling

Books

I Active Risk Management: Financial Models and Statistical Methods,Chapman and Hall/CRC, 2016. (Lai and Xing)

I Quantitative Trading: Algorithms, Analytics, Data, Models, Optimization,Chapman and Hall/CRC, 2017. (Guo, Lai, Shek and Wong: academicsand hedge funds).

Regulatory Technology since 2015

• Regulatory technology, also known as RegTech, is a relatively new field in the financial industry that uses IT to enhance regulatory monitoring, reporting and compliance and to standardize regulatory processes, thereby improving transparency, consistency and quality with reduced cost. By focusing on the digitization of manual reporting and compliance processes, e.g. in the context of KYC (“know your customer”) requirements, RegTech has offered considerable cost savings in financial services and regulation.

• Heralded “the new FinTech” by Deloitte that can provide “nimble, configurable, easy to integrate, reliable, secure and cost-efficient” regulatory solutions, RegTech has rapidly risen to prominence since 2015. London is increasingly being seen as the home of RegTech with the FCA as the governmental body to establish and promote RegTech. FCA envisioned “new approaches to “streamline AML (anti-money laundering) checks”, and the use of social media and biometrics to transform how customer due diligence is done. These new approaches include AI, machine learning, and blockchains.

Blockchain in RegTech

• The Forbes article (April 2018) describes the emerging powerful role of Blockchains in RegTech, saying: “In January, the crypto market proceed $700 billion – a new record. Yet this still-nascent sector, with the volatile currencies, novel but frenzied initial coin offerings (ICOs), and dearth of established use cases, is still far from being mainstream… China, South Korea, New Jersey are all clamping down on cryptocurrencies this month. On the other hand, other jurisdictions are taking a more proactive approach – Switzerland has published guidelines for ICOs, and Japan is enforcing stronger regulations for exchanges trading cryptocurrencies. The sector desperately needs professionalization and mechanisms for legitimization, and is where Coinfirm comes in. The two-year-old RegTech company is setting the benchmark for blockchain compliance… Working with companies across the market, Coinfirm is the architect behind a set of global standards for crypto and blockchain firms, as well as leading an industry working group on standard-setting and guidance.”

• The article continues with an interior with Pawel Kuskowski, CEO of Coinfirm. The interviewer says that the company’s plan for 2018 is “to become the gold standard for AML in the blockchain space, then to become the firm behind the regulatory technology for the entire sector, using big data and machine learning every day to enhance what blockchain offers. “