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DMBICONSULTANTS

DMBI Consultants Profile

DMBI is an innovative consultancy, founded in 2008, working in Data Science business.

DMBI supports Banks and production, distribution and service companies toimprove commercial performances, to optimize economic marginality in orderto support strategic decisions and integrating tecnology in:

• BUSINESS INTELLIGENCE (BI)

• DATA MINING (DM)

About Us

Finance FashionCORPORATE

AFFAIRS

PRESIDENT/MD

HUMAN RESOURCES

Retail

STAFF LINE/Operations -Business Development

DMBI is a team made up of professionals coordinated by the Managing Director (MD)who holds the role of both President and Managing Director.The organizational chart is divided into Staff, HR and Corporate Affairs (Finance,Administration, Office Mgmt), and Line functions which are organized according toindustries divided into Operations (already appointed consultancies) and BusinessDevelopment (prospective appointments).

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Technologies Used

• DBMS - Database Management System • ETL – Extraction Transformation & Loading

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Technologies Used

• OLAP - On-Line Analytical Processing • MDM – Master Data Management

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• Statistical Software

Technologies used

• Text Mining

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• Data Mining

Technologies used

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The Data Warehouse approach

DMBI

Business Partners and Main Clients

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From Certain Causation

to Probabilistic Causation*

Application fields in Finance sector

*(http://plato.stanford.edu/entries/causation-probabilistic/)

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Probabilistic logicIntroduction

During the first 50 years of its life, Informatics was focused on Certain

Causation. Today, new trends are instead focused on Probabilistic Causation.

This last application field was studied by Patrick Suppes (1922-

2014), American philosopher and professor Emeritus at Stanford University

(USA) who made significant contributions to philosophy of science, the theory

of measurement and the foundations of quantum mechanics. He

collaborated with Bruno De Finetti (1906 – 1985),Italian

probabilistic, statistician and actuary, noted for the "operational subjective"

conception of probability.

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Probabilistic CausationIntroduction

There are so many Probabilistic Theory’s application areas:

everyday we use a lot of software and services based on

probabilistic logic. For example:

Global Navigation Satellite Systems;

BioInformatics & Co (*);

Automatic and AssistedDriving Systems.

(*Cfr How tecnology will eat medicine – VivekWadhwa. The WSJournal 15/01/2015)

Probabilistic CausationApplication Fields

DMBI works in Finance (with Lending Institutions , Insurance

Companies and PosteItaliane), Retail and Fashion projects, using

Probabilistic Logic computational applications.

Our consultants manage Data Mining and Business Intelligence

through software and applications, capable of dealing with

probabilistic processes.

Probabilistic CausationApplication Fields- Value at Risk (VaR)

«VaR is the Maximum Loss that every financial instrument may

suffer if adverse events were to occur in a given lapse of time»

According to Basel Committee on Banking Supervision (*), each Bank must calculate a daily VaR for every Portfolio asset.

The timeline is 10 days and the confidence interval is 99%.

(*Basel Committee is an International organization established by Central Bank governments of the G10Countries at the end of 1974 and supported by the Bank for International Settlements.Its focus is on improving the cooperation between the central banks and the agencies with the common goal ofpromoting the monetary and financial solidity.The Committee coordinates the balance of responsibility regarding security between national authorities, tosupervise worldwide banking activities.The Committee meets 4 times per year and its current President is Stefan Ingves, head of Bank of Sweden).

Probabilistic CausationApplication Fields-Probability of default (PD)

«The Probability of Default is the probability that thecounterparty doesn’t return the credit and itsaccumulated interests»

Banks estimate the Probability of Default to evaluate the Credit Risk for eachcredit.

Example: if the Banks evaluate a 5% of probability of default, it means thatthey’ll evaluate a 5% of expected loss of their credits (assuming they have nowarranties).

Probabilistic CausationApplication Fields-Solvency

«Solvency II (Directive 2009/138/CE) is a EU Directive which broadens the Basel regulation to the InsuranceIndustry»

In November 2003, The European Commission established a permanent committeewhich had the role of editing a preliminary law that would manage the risk in theInsurance Industry. The CEIOPS (Committee of European Insurance and OccupationalPensions Supervisors) is an international coordination for Member States authorities inthe Insurance Industry and pensions.

In 2005, CEIOPS appointed the IAA (International Actuarial Association), aninternational association which standardises the corporate internal accounting,making a list of Insurance industry risks not covered by Basel 2, which is designedinstead for banking industry.

Probabilistic CausationApplication Fields- Accounting

Analysis switches from IAS 39 (International Accounting Standards), mainly concentrating onaccounting approaches, to IFRS 9 (International Financial Reporting Standards) focusing insteadon the reporting and the qualitative and/or descriptive assessment of financial tools and complex

phenomena.

IAS 39 IFRS 9

Standardized implementation to evaluate financial tools Based on accounting standards and guidelines to evaluatefinancial tools

Ex-post calculation of liabilities and assets: historical costvaluation or at past purchase value and related depreciatedcosts

Ex-ante calculation of liabilities and assets: Fair Value valuation, or rather, market value basis of valuation

Liabilities and assets calculation without consideringadditional information

Evaluation and expected value of liabilities and assetsconsidering the available information ( past events, probability, conditions etc.)

However, the new IFRS model introduces the Probabilistic Causation in accounting. For example, this model analyzes the credits considering the expected loss with bettertimeliness ( a weakness of the past standard, since it only applied accountancy rules). The first aims of the new standards are:• providing useful information about the expected loss of financial tools; • establishing a better connection between accounting and risk analysis.

Probabilistic CausationApplication fields and developmental directions

Developmental DirectionsValue at Risk (VaR) – Extreme Value Theory

The Extreme Value Theory is a branch of Risk Theory studying theextreme deviations from the central values of a probabilitydistribution; its results have remarkable relevance with regard to therisk evaluation of rare events (such as a collapse of the stock exchange)and natural disasters.

The traditional models used for the VaR evaluation based on varianceand covariance have proved to be «weak» in risk appraisal over longterm evaluation.

Developmental DirectionsProbability of default (PD) – Social networks

PD’s evaluation could evolve along more efficient and realisticassessment standards in the future.

The social networks influence the individual solvency capability in thePEER-TO-PEER LENDING (Ankit Kumar, Matan Zinger).

The social networks play a crucial role, especially in the “business”commitments (in other words, the companies commitments).

Ankit Kumar e Matan Zinger are two researchers at Stanford University who studiedweb marketplaces, which intermediate individual loans; this means, loans deliveredbetween private counterparties (people or firms). That’s PEER-TO-PEER LENDING!

Developmental DirectionsProbability of default (PD) - Statistical Learning

As we can infer from the social network approach, the new trends ofPD evaluation aim at making a personal evaluation, focusing on theessential elements of the evaluated individual.

Vapnik’s Statistical Learning is a good example of PD evaluation. Thistheory has developed predictive models based on big data sets. It hasmany application fields, such as computer vision, speechrecognition, bioinformatics and sport.

Vladimir Naumovič Vapnik (Владимир Наумович Вапник) is a sovieticmathematician and statistician. He is one of the most important Vapnik-Červonenkistheory creators. This theory, developed between the 60’s and the 90’s, explains thelearning process (computational learning) using a statistical point of view. Hence, it’sthe base of the predictive functions modeling.

DMBI ConsultantsRecruiting, education, experience

DMBI has developed a positive practice focused on inserting young talents in the DataMining and Business Intelligence branch in the consultancy world.

The new graduates are interviewed by occupational psychologists to evaluate theirattitude to:

Working with clients with maximum exposure

Working in mixed teams (Clients/DMBI/Partners) characterized by a high degree ofprofessionalism

DMBI Management makes a further level selection to evaluate the attitudinal and,especially, the technical requirements with special care to the candidate’s potentialgrowth.

The admission process involves an induction plan organized into two levels:

I. Internal experience: mentoring and self-training, lessons, workshops including theexperience of senior consultants.

II. External experience: training on the job during the consultancy, with the supportof senior consultants.

DMBI ConsultantsRecruiting and training process

DMBI ConsultantsProjects (example I)

DMBI RESOURCE: Consultant Executive at PosteMobile from April toDecember 2015.

PROJECT: Transaction Monitoring

AIM OF PROJECT: Monitoring and management of financial fraud inBancoPosta

DESCRIPTION OF ACTIVITY: In this project, the job of DMBI’s consultantinvolved the use of SAS SOCIAL NETWORK ANALYSIS (SNA) webapplication.

DMBI CONTRIBUTION: DMBI’s role resided firstly in writing and correctingcodes, in order to define the ALERT rules on which effective fraud could belater verified. Once the Monitoring Center of Turin has detected the fraud,DMBI’s consultant identifies the dispositions linked to the fraud, using thesocial networks which were found through the use of SAS SNA.

DMBI ConsultantsProjects (example II)

ACTIVITY: Consultant Executive for Credit Risk at Banca Sella through KPMG, from August toDecember 2015.

PROJECT: Loss Given Default

AIM OF PROJECT: Construction of an LGD calculation model according to the AIRB (AdvancedInternational Rating-Based) requirements of Basel II for the credit portfolios

DESCRIPTION OF ACTIVITY: DMBI’s analyst has used SAS to work out historical informationneeded to evaluate the LGD. The datasets considered were:

• CIRCUIT A: components to determine LGD about non-performing loans or short-term credit repayments

• CIRCUIT B: only components in default.

Each circuit is divided into different datasets (such as ANAGRAPHIC, NON PERFORMING LOANS ,EXPOSURE, BANKING MOVEMENTS, WARRANTY MOVEMENTS, etc ) and is composed of differentvariables, i.e. REFERENCE (example: Reference for registry, reference for list of loans, etc.) GUIDEDATE, ABI CODEX (the same of Banca Sella) and the COUNTERPARTY CODE.

DMBI CONTRIBUTION: SAS connection through coding of the variables listed above for some datasets from circuit A, such as anagraphic, non-performing loans , exposures , banking movements , so as to reveal temporary transitions (performing “bonis”, non-performingloans, short-term credit repayments, past due ) useful for the calculation of LGD .

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DMBI Consultants – Contacts

Dmbi Consultants SrlPiazza Spada 700044 Frascati, Roma, ItaliaPhone: +39 06 9422 421Website: www.dmbi.orgLinkedin: DMBI Consultants srl

Dr. Antonello GiannellaPresident & Managing DirectorMobile: +39 335 5334394Email: a.giannella@dmbi.org

Dr. Marco CentioniHR & Legal AffairsMobile: +39 338 4220642Email: m.centioni@dmbi.org