Open Source Investor Services “OSIS”
Unbundling credit risk management
PRIVATE(AND(CONFIDENTIAL(
REACTION ON THE CRISIS CREATED A LARGER CRISIS
! Crisis(came,(nobody(could(explain(the(risk,(due(to(lack(of(clean,(conDnuous(and(
consistent(data(
! 11(years(ago(the(creaDon(of(banking(consorDum(Global(Credit(Data(
! Knowledge(about(availability,(quality(and(usage(of(data(
! Huge(problem,(big(opportunity.(
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PRIVATE(AND(CONFIDENTIAL(
Open Source investor services
! Based(in(The(Hague,(The(Netherlands(
! Founded(December(2010(by(two(former(bankers(
! Goal:(making(complex(credit(risk(analysis(accessible(and(easy(to(use(
! 5(employees(and(5(freelancers(
! AcDve(with(exisDng(product(in(the(enDre(credit(risk(value(chain(
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Data"quality"
Data"storage"
Data"analysis"
Data"modeling"
Ra5ng"&"Valua5on" Disclosure"
PRIVATE(AND(CONFIDENTIAL(
A healthy mix of innovations 17"
""
3rd"genera5on"of"models"
Cloud"Compu5ng"
Quality"Data"
o Our(plaSorm(is(a(combinaDon(of(new(technologies:(
o BeTer(data(quality(
o New(generaDon(of(credit(models(
o Online(access(and(easy(of(use(
o Complete(cloud(infrastructure(
PRIVATE(AND(CONFIDENTIAL(
And as a result
! Models(get(automaDcally(recalibrated(
! More(sophisDcated,(cost(efficient,(Dmely(and(granular(results(
! We(already(can(take(over(the(risk(management(reporDng(and(models(framework(
of:(
– 122(banks’(mortgage(porSolios(
– 50(banks’(SME(porSolio((Pd(models(are(their(way)(
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PRIVATE(AND(CONFIDENTIAL(
Where we are today 20"
Each quarter we apply 8 billion consistency checks on a pool of mortgages of 122 banks
We have created a library of over 20,000 stress test models used during EU-wide stress test.
We have access to and actively using the best data in the industry
We estimate PD’s and LGD’s of 8 million mortgages from 122 banks and each quarter we re-calibrate our models based on new information.
Our dynamic Bayesian models provide a better understanding about the credit risk of an ABS pool than the rating agencies have.
With our point-in-time approach the analyst can also add macro stresses to the model.
PRIVATE(AND(CONFIDENTIAL(
Changes in the lending market
o According(to(research(from(Deutsche(Bank,(Barclays(and(Goldman(Sachs(about(USD(
8,000bn(of(loans(will(shi^(from(the(regulated(banking(sector(
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PRIVATE(AND(CONFIDENTIAL(
Disintermediation 9"
"End"Investor"
Borrower"
1."Asset"Manager/"Hedge"Fund"
3."Investment"Bank"
2."Credit"ra5ng"agency"
! Much(of(the(analysis(of(the(End(Investor(is(outsourced(to(
intermediaries(
! 1_3(are(making(the(best(money(
! New(techniques(like(cloud(compuDng(make(outsourcing(of(risk(
management(easier(providing(room(to(insource(the(analyDcs(
! Investors(back(in(the(driver(seat,(making(lending(more(efficient(
– Lower(costs(
– BeTer(understanding(hence(lower"informa5on"asymmetry!"
1.
Bank"
PRIVATE(AND(CONFIDENTIAL(
ALAN TURING USED BAYESIAN ANALYSIS TO CRACK ENIGMA 10"
PRIVATE(AND(CONFIDENTIAL(
Bayesian Updating: The tennis player
! Assume(the(tennis(player(gets(new(unknown(tennis(balls(
((
! Before(returning(the(ball(the(tennis(player(need(to(make((
(((((((an(hypothesis(about(the(balls(
! A^er(returning(the(ball(the(tennis(player(will(add(his((
(((((((observaDons(about(the(ball(to(a(new(hypothesis(
(
! A^er(some(Dme(his(hypothesis(will(stabilize(and(he((
(((((((will(gain(control(on(the(tennis(ball(
(
(
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Prior"
Likelihood"
Posterior"
PRIVATE(AND(CONFIDENTIAL(
Conclusion
! The(only(thing(we(have(to(fear(is(fear(is(itself:(it(is(uncertainty(we(need(to(take(care,(
about(more(than(high(volaDlity(of(losses(
! Much(informaDon(is(there,(quesDon(is:(
– How(to(make(it(available,(
– Understandable(and(
– Easy(of(use.(
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