Open Source Investor Services “OSIS”

Unbundling credit risk management

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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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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"

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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.

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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"

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ALAN TURING USED BAYESIAN ANALYSIS TO CRACK ENIGMA 10"

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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"

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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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