¡FELIZ DÍA DE LA MADRE!

The following information is used for educational purposes only.


                                   ¡FELIZ DÍA DE LA MADRE!

Maravillosa, moderna, maternal

Alegre, amorosa, angelical

Divertida, dinámica, decidida

Respetuosa, romántica,realista

Especial,esmerada, encantadora


Estas son sólo algunas de las cualidades que mi madre tenía e 

inculcaba con su ejemplo de palabras y acciones.Ejemplo de 

buena MUJER con todas las letras y en mayúsculas.

Gracias MAMÁ por ser la mejor de todas, gracias MAMÁ por 

amarme y enseñarme todo lo bueno que tengo e intento ser cada 

día.C.M.

Fuente: Google Images/Palabras de Clara Moras.

EDITORIALES | POLÍTICA ECONÓMICA-Palabras sin peso

The following information is used for educational purposes only.


EDITORIALES | POLÍTICA ECONÓMICA

Palabras sin peso

No habrá manera de poner a la Argentina de pie, de avanzar hacia el desarrollo sostenido, de generar empleo o de superar la pobreza si se carece de moneda

20 de Octubre de 2019

Ninguna propuesta económica tiene sentido si en su primer capítulo ignora al peso. Es decir, a la moneda. Los discursos que omiten esta cuestión preliminar son palabras huecas, palabras sin peso.

La Argentina no se pondrá de pie, ni habrá justicia social, ni empleo, ni salud, ni educación, ni inclusión, ni atención a la vejez, ni desarrollo industrial, si carece de moneda. De nada vale analizar soluciones "a la uruguaya" o "a la portuguesa" para atender los vencimientos de la deuda externa, pues ningún país sin moneda puede proyectar crecimiento para sustentar pagos creíbles. Ningún pacto social tendrá una salida incruenta, sin resolver esta cuestión primero.

Sin moneda, no hay inversión. Sin moneda, no hay crédito. Sin moneda, hay inflación y tasas exorbitantes. Sin moneda, hay especulación y fuga de capitales. Sin moneda, hay pobreza. Sin moneda, hay paros y crispación; piquetes, bombos y encapuchados. Sin moneda no hay clases, no salen los aviones, no atienden los hospitales. Sin moneda, todo es anomia, disputa y frustración.


La moneda es requisito del orden institucional, vértice del acuerdo de convivencia, símbolo de credibilidad recíproca y custodia de sudores cotidianos, transformados en ahorros. Carecer de ella es un fenómeno exótico, pues todas las naciones lo han comprendido, esforzándose por lograr monedas fuertes, de alto poder adquisitivo, sobre la base de economías competitivas. Salvo, claro está, aquellos que viven en el caos, como Venezuela, Zimbabue o Sudán.

El peso argentino no reúne esas condiciones morales y no alcanza a ser moneda. Solo billetes de inmediata obsolescencia o lenguaje binario en la contabilidad de bancos, que pronto se desactualiza. El peso no deseado es una promesa incumplida, letra muerta, estafa colectiva. Despojado de contenido institucional, avergüenza a su emisor, encerrado entre el dólar y la tasa de interés para evitar su extinción final. Los políticos se quejan de la inflación y los dirigentes sociales, de la pobreza, pero nadie propone atacarla de raíz, eliminando sus causas. Pronuncian palabras sin peso y, como en el diván, evitan hablar de lo que más duele.

El presidente Mauricio Macri no advirtió que el gradualismo, al no reducir el gasto público y mantener latente el riesgo confiscatorio, era ineficaz para recrear la demanda de dinero, incrementar el ahorro interno y expandir el crédito. En otras palabras, para llenar las heladeras. Aun así, y a pesar de ese error de diagnóstico, transmite las convicciones republicanas indispensables para corregir su yerro y lograr que nuestro peso sea una moneda.

Por el contrario, la oposición parece ignorar el desafío que implica revertir la pésima reputación de la Argentina, incumplidora serial de contratos invocando emergencias de su autoría. No basta con designar un gabinete de lujo o un economista laureado para generar confianza. Es indispensable tener principios firmes y adoptar compromisos creíbles para que los argentinos volvamos al peso. No basta con una alquimia monetaria o un pacto corporativo: se requiere un cambio fundacional que recomponga las expectativas.

Relegar este dilema como asunto marginal parece sugerir que la moneda es una molestia, un prejuicio neoliberal para bloquear el crecimiento que merecemos; un artificio irrelevante para un desarrollo que debe ser impulsado por decisiones gubernamentales y no por actores privados.

Esa aversión populista al dinero tiene un trasfondo ideológico. El capitalismo introdujo la división del trabajo, la especialización productiva y -según Carlos Marx- la alienación del hombre, quien se habría convertido en eslabón de procesos fuera de su control. La moneda sería el instrumento perverso de ese mecanismo, pues habilita el comercio, la formación de capital y la renta financiera. De allí la (falsa) dicotomía entre las actividades productivas y las "especulativas", con especial desdén por el rol de los bancos, los fondos de pensión, las prepagas de salud u otros gestores de ahorros.

Está claro que la fórmula Fernández-Fernández no propone abandonar el sistema capitalista para adoptar un régimen colectivista, sin propiedad privada. Pero la situación del país es tan grave, con setenta años de inflación y ocho defaults en su haber, que revertir esa historia requiere un mensaje unívoco, convencido y convincente, sin el cual ningún programa será cumplible. Sostener que "la seguridad jurídica es una palabra horrible" (Axel Kicillof); que "la inflación es fruto de la puja distributiva" (Cristina Kirchner); que "todos los precios son políticos" (Augusto Costa); "que un Estado no necesita endeudarse porque puede emitir" (Fernanda Vallejos), o la eventual reforma constitucional para fracturar la hegemonía dominante presagia un regreso a modelos fracasados.

En Cuba no hay puja distributiva: los pesos cubanos (CUC) son cupones para racionar bienes conforme al criterio de los Comités de Defensa de la Revolución en almacenes predeterminados, con estanterías vacías. En los países de la órbita soviética, el rublo no requería seguridad jurídica, ni preocupaba la emisión, pues la vivienda, la salud, la educación, el transporte y el turismo eran asignados directamente por el Estado, hasta colapsar hace 30 años. En Venezuela, por otro lado, la hiperinflación ha quitado todo valor al bolívar, requiriendo tanta cantidad de billetes físicos que la emisión no alcanza, como en la Alemania de Weimar. De hecho, se recurre al trueque de bienes o servicios para intentar comer o curarse.

En la Argentina siempre se opta por la alternativa extractiva con tal de evitar reformas de fondo. Se consume capital en lugar de aumentar la productividad del esfuerzo cotidiano. En algún momento, fueron los lingotes de oro en el Banco Central; luego los depósitos bancarios; en tiempos más recientes, la soja, las AFJP o YPF. Ahora, la salvación serían los yacimientos de Vaca Muerta, en la esperanza de que los esquistos bituminosos se hagan cargo del empleo público, de las jubilaciones sin aportes, de los subsidios económicos, de los planes sociales y hasta de las 72 pymes que conforman los asesores senatoriales.

Esta vez, no habrá tiempo para reinventar la rueda. Si los programas de los candidatos y los eventuales pactos sociales no prevén una solución rápida y definitiva para recuperar la moneda, la propia estabilidad política de quien asuma el próximo 10 de diciembre estará en juego. Ha llegado el momento de asumir ese compromiso y tener el coraje de decir palabras con peso.


Fuente:https://www.lanacion.com.ar/editoriales/palabras-sin-peso-nid2298759

AML/KYC-Making your KYC remediation efforts risk and value-based, by Mette Gade,Daniel Mikkelsen,Dan Williams

The following information is used for educational purposes only.


Making your KYC remediation efforts risk and value-based

Banks are sitting on large know-your-customer (KYC) and due diligence backlogs. Four steps can cut them quickly and improve the customer experience by ensuring remediation efforts are better aligned with business value and the potential risks each customer poses.

By Mette Gade,Daniel Mikkelsen,Dan Williams


August 23, 2019.Banks worldwide have paid over $30 billion in penalties since 2009 for failing to crack down on financial crime. Add to that the reputational cost of getting embroiled in money laundering scandals and it’s not hard to see why banks are so keen to meet anti-money laundering (AML) requirements. Yet many are overwhelmed by the very first steps of the process, finding themselves sitting on large know-your-customer (KYC) and due diligence backlogs.

Why? Firstly because of the scale of the task. Collecting, validating and continually updating data for millions of customers is time consuming, and frequently changing requirements means that approaches to KYC needs to be rethought. In addition, there are plenty of inefficiencies in the process, which remains largely manual to this day. Previously-recorded information is buried in various paper or electronic files, proving hard to access and aggregate. Queries ping pong back and forth between customers, frontline and back-office staff. Customer insights and lessons learned during due diligence aren’t taken into account in monitoring activities or when setting controls.

It needn’t be like this. The same ‘digital first’ approach that has transformed banks’ commercial and operational performance with digital technology and agile methodologies can similarly transform KYC and due diligence. Here are four steps that can quickly cut the backlog, improve the customer experience and, importantly, shift the focus to optimizing value while mitigating risks.

Four steps to ensure a risk-based KYC and due diligence remediation

1.To manage both risk and value, segment customers more finely. Most banks expend disproportionate effort on customers who pose very little or no risk.

2.Deploy self-service solutions that are risk-sensitive and carry minimal execution costs. Self-service should be the default option for customers providing KYC information. By automatically posing more questions to customers whose responses suggest higher risk, the burden on less-risky customers is kept to a minimum.

3.Tailor and track remediation efforts at the individual customer level. This will inform required actions and provide operations, the board and regulators a clear view of how remediation efforts are faring.

4.To quicken progress, make use of third-party data, external providers and artificial intelligence (AI). There are plenty of off-the-shelf solutions and data providers that can help quickly stitch together an integrated solution. AI can then accelerate learnings from these outputs.

1. To manage both risk and value, segment customers more finely

Existing AML customer risk-rating models will likely identify between 0 and 5 percent of customers as potentially high risk-although in some banking segments this proportion can be higher. These customers are prioritized and undergo enhanced due diligence. The remaining 90-plus percent, however, are grouped into two or three segments, or occasionally only one. As a result most customers undergo similar or barely differentiated levels of KYC and due diligence, with banks often devoting unnecessary resources on the majority of their customers posing minimal or no risk.

A model that segments customers more finely – perhaps into as many as 10 to 30 categories – can ensure remediation efforts are aligned with the level of risk. Building one takes time, however. In the interim, consider a pragmatic approach in keeping with agile principles that strive for incremental improvements and fast learning, using available customer information. For example, customers who only have a deposit account, have pension products, whose transactions are below a certain threshold or whose accounts are inactive, typically pose limited risks. As many as 75 percent of customers may fall into this category. On the other hand, customers who use a range of digital channels or have used a digital channel for onboarding and lack in-person identity verification would fall into a higher priority category.

Often, in-house customer data can be supplemented with external data. Take, for example, knowledge that a customer is a student. One bank used public records on the average wealth of university students in different regions to understand “normal” wealth and banking activity for these customers, enabling IT to categorize most into a lower-risk category.

This finer segmentation can be used to set appropriate remediation activities, choosing between proactive or reactive contact with customers, for example, and determining various monitoring procedures and controls, such as an automatic alert if a customer moves into a riskier category. Segmentation can also address some regulatory priorities, such as understanding the expected banking activity and source of wealth of a customer, using available data, without the need to ask the customer.

2. Deploy self-service solutions that are risk sensitive and carry minimal execution costs

To lighten banks’ workload, a full self-service solution should be the default option for customers undergoing KYC and due diligence in high volume segments – that is, retail banking, small corporates and, potentially, high wealth customers. Self-service can reduce marginal execution costs to near zero. Because some customers will need assistance, the solution should be configured so that staff can access it as well, whether to help customers stuck on a certain question or requesting full assistance. Alternatively, staff can contact customers to gather preliminary information, then ask them to complete the process online.

Importantly, self-service solutions should be risk-sensitive, automatically increasing the number of questions proportionate to a customer profile’s implied risk. This eases the burden on low-risk customers, ensures the proper information is collected for higher risk customers, and quickly highlights areas where manual intervention may be required.

Self-service solutions will not be perfect from the outset – which is why they must be configured so that improvements can be rapidly implemented. One bank found customers stumbled over a question requiring a tax identification number. Quick rewording solved the problem in minutes – something that would have taken a month or more in a typical IT release process.

For customers who prefer to visit a branch or speak on the phone to complete the KYC process, bear in mind that the necessary conversations around spending patterns and sources of wealth also provide an opportunity to offer advice on other products and services, such as investment planning, pension products, and mortgage re-financing.

3. Tailor and track the remediation efforts at the individual customer-level

Remediation efforts will be more powerful if teams follow the approach used by digital marketers. Would-be customers’ online progress is digitally tracked through a “sales funnel”, helping marketers learn where and how best to intervene to keep them moving from the initial consideration of a purchase through to a sale.

In the same way, banks can track each customer’s progress though the KYC and due diligence process, determining appropriate actions at each stage depending on the customer’s preferences, behavioral profile and risk categorization. In marketing language, each customer is a segment of one. For example, an automated pop-up reminder to submit information in a mobile banking app might suffice for many customers. But some may need a second message emphasizing the importance of countering financial crime, and still others a third notifying them that their account has been blocked until the information is submitted. Banks may discover that certain customers respond better to a call than an email, or a better time of day at which to reach them.

It will, of course, take time for banks to clear remediation backlogs and become fully compliant. But an agile approach ensures continuous improvement. Therefore, tracking the remediation status of each segment, expected completion of the remediation and required escalations and sanctions is essential. Make sure progress in meeting timelines and any lessons learned are clear to all. Only then will teams be able to improve the remediation process, and executive management gain comfort with it. Importantly, this transparency also broadens discussions with boards and regulators from a singular focus on whether deadlines have been met to one that also considers whether the highest risks are being appropriately addressed.

4. To quicken progress, make use of third-party data, external providers and artificial intelligence (AI)

In addition to using the vast amount of internal data for pre-population or validation, plenty of help is available for getting the data you need. RegTechs and other providers can provide lists of beneficial owners, politically-exposed persons (PEPs), or those who feature negatively in media coverage. Public registries and utilities can, in some countries, supply tax and salary records. And don’t overlook data generated from customers’ digital footprints, if local regulations allow and customers consent, or location data that can verify a customer’s presence close to a given address.

AI, meanwhile, will not only speed the KYC and due diligence process, but help to improve it continuously. Optical character recognition (OCR), for example, can extract information from old customer records for validation or pre-population. Fuzzy logic can reduce the number of false positives generated when customers or colleagues make typing errors. AI can also ensure that learnings from transaction monitoring or false positives are used to refine initial KYC questions, optimizing not just the KYC process but the full AML value chain.

While the mindset should shift to “digital first,” some manual intervention will still be needed. Make good use of smart workflow tools to ease case handling, as their numbers are growing. The best AML value chains are typically those that stitch together the best platform providers and efficient AI engines for continuous learning loops. No single vendor provides everything you need.

Ultimately, the most important AML value chains may prove to be those established by banks and financial institutions to pool their resources in an AML utility. The aim is not only to share technology costs, but to derive more powerful insights from collective data and crack down harder on financial crime.


Source:https://www.mckinsey.com/industries/financial-services/our-insights/banking-matters/making-your-kyc-remediation-efforts-risk-and-value-based

AML-Flushing out the money launderers with better customer risk-rating models, by Daniel Mikkelsen, Azra Pravdic, and Bryan Richardson

The following information is used for educational purposes only.


Flushing out the money launderers with better customer risk-rating models

September 2019

By Daniel Mikkelsen, Azra Pravdic, and Bryan Richardson


Dramatically improve detection rates by simplifying model architecture, fixing underlying data, and using machine-learning algorithms to identify high-risk behavior.

Money laundering is a serious problem for the global economy, with the sums involved variously estimated at between 2 and 5 percent of global GDP.1 Financial institutions are required by regulators to help combat money laundering and have invested billions of dollars to comply. Nevertheless, the penalties these institutions incur for compliance failure continue to rise: in 2017, fines were widely reported as having totaled $321 billion since 2008 and $42 billion in 2016 alone.2 This suggests that regulators are determined to crack down but also that criminals are becoming increasingly sophisticated.

Customer risk-rating models are one of three primary tools used by financial institutions to detect money laundering. The models deployed by most institutions today are based on an assessment of risk factors such as the customer’s occupation, salary, and the banking products used. The information is collected when an account is opened, but it is infrequently updated. These inputs, along with the weighting each is given, are used to calculate a risk-rating score. But the scores are notoriously inaccurate, not only failing to detect some high-risk customers, but often misclassifying thousands of low-risk customers as high risk. This forces institutions to review vast numbers of cases unnecessarily, which in turn drives up their costs, annoys many low-risk customers because of the extra scrutiny, and dilutes the effectiveness of anti–money laundering (AML) efforts as resources are concentrated in the wrong place.

In the past, financial institutions have hesitated to do things differently, uncertain how regulators might respond. Yet regulators around the world are now encouraging innovative approaches to combat money laundering and leading banks are responding by testing prototype versions of new processes and practices.3 Some of those leaders have adopted the approach to customer risk rating described in this article, which integrates aspects of two other important AML tools: transaction monitoring and customer screening. The approach identifies high-risk customers far more effectively than the method used by most financial institutions today, in some cases reducing the number of incorrectly labeled high-risk customers by between 25 and 50 percent. It also uses AML resources far more efficiently.

Best practice in customer risk rating

To adopt the new generation of customer risk-rating models, financial institutions are applying five best practices: they simplify the architecture of their models, improve the quality of their data, introduce statistical analysis to complement expert judgment, continuously update customer profiles while also considering customer behavior, and deploy machine learning and network science tools.

1. Simplify the model architecture

Most AML models are overly complex. The factors used to measure customer risk have evolved and multiplied in response to regulatory requirements and perceptions of customer risk but still are not comprehensive. Models often contain risk factors that fail to distinguish between high- and low-risk countries, for example. In addition, methodologies for assessing risk vary by line of business and model. Different risk factors might be used for different customer segments, and even when the same factor is used it is often in name only. Different lines of business might use different occupational risk-rating scales, for instance. All this impairs the accuracy of risk scores and raises the cost of maintaining the models. Furthermore, a web of legacy and overlapping factors can make it difficult to ensure that important rules are effectively implemented. A person exposed to political risk might slip through screening processes if different business units use different checklists, for example.

Under the new approach, leading institutions examine their AML programs holistically, first aligning all models to a consistent set of risk factors, then determining the specific inputs that are relevant for each line of business (Exhibit 1). The approach not only identifies risk more effectively but does so more efficiently, as different businesses can share the investments needed to develop tools, approaches, standards, and data pipelines.

Exhibit 1 (See source article)

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2. Improve data quality

Poor data quality is the single biggest contributor to the poor performance of customer risk-rating models. Incorrect know-your-customer (KYC) information, missing information on company suppliers, and erroneous business descriptions impair the effectiveness of screening tools and needlessly raise the workload of investigation teams. In many institutions, over half the cases reviewed have been labeled high risk simply due to poor data quality.

The problem can be a hard one to solve as the source of poor data is often unclear. Any one of the systems that data passes through, including the process for collecting data, could account for identifying occupations incorrectly, for example. However, machine-learning algorithms can search exhaustively through subsegments of the data to identify where quality issues are concentrated, helping investigators identify and resolve them. Sometimes, natural-language processing (NLP) can help. One bank discovered that a great many cases were flagged as high risk and had to be reviewed because customers described themselves as a doctor or MD, when the system only recognized “physician” as an occupation. NLP algorithms were used to conduct semantic analysis and quickly fix the problem, helping to reduce the enhanced due-diligence backlog by more than 10 percent. In the longer term, however, better-quality data is the solution.

3. Complement expert judgment with statistical analysis

Financial institutions have traditionally relied on experts, as well as regulatory guidance, to identify the inputs used in risk-rating-score models and decide how to weight them. But different inputs from different experts contribute to unnecessary complexity and many bespoke rules. Moreover, because risk scores depend in large measure on the experts’ professional experience, checking their relevance or accuracy can be difficult. Statistically calibrated models tend to be simpler. And, importantly, they are more accurate, generating significantly fewer false-positive high-risk cases.

Building a statistically calibrated model might seem a difficult task given the limited amount of data available concerning actual money-laundering cases. In the United States, suspicious cases are passed to government authorities that will not confirm whether the customer has laundered money. But high-risk cases can be used to train a model instead. A file review by investigators can help label an appropriate number of cases—perhaps 1,000—as high or low risk based on their own risk assessment. This data set can then be used to calibrate the parameters in a model by using statistical techniques such as regression. It is critical that the sample reviewed by investigators contains enough high-risk cases and that the rating is peer-reviewed to mitigate any bias.

Experts still play an important role in model development, therefore. They are best qualified to identify the risk factors that a model requires as a starting point. And they can spot spurious inputs that might result from statistical analysis alone. However, statistical algorithms specify optimal weightings for each risk factor, provide a fact base for removing inputs that are not informative, and simplify the model by, for example, removing correlated model inputs.

4. Continuously update customer profiles while also considering behavior

Most customer risk-rating models today take a static view of a customer’s profile—his or her current residence or occupation, for example. However, the information in a profile can become quickly outdated: most banks rely on customers to update their own information, which they do infrequently at best. A more effective risk-rating model updates customer information continuously, flagging a change of address to a high-risk country, for example. A further issue with profiles in general is that they are of limited value unless institutions are considering a person’s behavior as well. We have found that simply knowing a customer’s occupation or the banking products they use, for example, does not necessarily add predictive value to a model. More telling is whether the customer’s transaction behavior is in line with what would be expected given a stated occupation, or how the customer uses a product.

Take checking accounts. These are regarded as a risk factor, as they are used for cash deposits. But most banking customers have a checking account. So, while product risk is an important factor to consider, so too are behavioral variables. Evidence shows that customers with deeper banking relationships tend to be lower risk, which means customers with a checking account as well as other products are less likely to be high risk. The number of in-person visits to a bank might also help determine more accurately whether a customer with a checking account posed a high risk, as would his or her transaction behavior—the number and value of cash transactions and any cross-border activity. Connecting the insights from transaction-monitoring models with customer risk-rating models can significantly improve the effectiveness of the latter.

While statistically calibrated risk-rating models perform better than manually calibrated ones, machine learning and network science can further improve performance.

5. Deploy machine learning and network science tools

While statistically calibrated risk-rating models perform better than manually calibrated ones, machine learning and network science can further improve performance.

The list of possible model inputs is long, and many on the list are highly correlated and correspond to risk in varying degrees. Machine-learning tools can analyze all this. Feature-selection algorithms that are assumption-free can review thousands of potential model inputs to help identify the most relevant features, while variable clustering can remove redundant model inputs. Predictive algorithms (decision trees and adaptive boosting, for example) can help reveal the most predictive risk factors and combined indicators of high-risk customers—perhaps those with just one product, who do not pay bills but who transfer round-figure dollar sums internationally. In addition, machine-learning approaches can build competitive benchmark models to test model accuracy, and, as mentioned above, they can help fix data-quality issues.

Network science is also emerging as a powerful tool. Here, internal and external data are combined to reveal networks that, when aligned to known high-risk typologies, can be used as model inputs. For example, a bank’s usual AML-monitoring process would not pick up connections between four or five accounts steadily accruing small, irregular deposits that are then wired to a merchant account for the purchase of an asset—a boat perhaps. The individual activity does not raise alarm bells. Different customers could simply be purchasing boats from the same merchant. Add in more data however—GPS coordinates of commonly used ATMs for instance—and the transactions start to look suspicious because of the connections between the accounts (Exhibit 2). This type of analysis could discover new, important inputs for risk-rating models. In this instance, it might be a network risk score that measures the risk of transaction structuring—that is, the regular transfer of small amounts intended to avoid transaction-monitoring thresholds.

Exhibit 2 (See source article)

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Although such approaches can be powerful, it is important that models remain transparent. Investigators need to understand the reasoning behind a model’s decisions and ensure it is not biased against certain groups of customers. Many institutions are experimenting with machine-based approaches combined with transparency techniques such as LIME or Shapley values that explain why the model classifies customers as high risk.

Moving ahead

Some banks have already introduced many of the five best practices. Others have further to go. We see three horizons in the maturity of customer risk-rating models and, hence, their effectiveness and efficiency (Exhibit 3).

Exhibit 3 (See source article)

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Sidebar (See source article)

The journey toward sophisticated risk-rating models


Most banks are currently on horizon one, using models that are manually calibrated and give a periodic snapshot of the customer’s profile. On horizon two, statistical models use customer information that is regularly updated to rate customer risk more accurately. Horizon three is more sophisticated still. To complement information from customers’ profiles, institutions use network analytics to construct a behavioral view of how money moves around their customers’ accounts. Customer risk scores are computed via machine-learning approaches utilizing transparency techniques to explain the scores and accelerate investigations. And customer data are updated continuously while external data, such as property records, are used to flag potential data-quality issues and prioritize remediation.

Financial institutions can take practical steps to start their journey toward horizon three, a process that may take anywhere from 12 to 36 months to complete (see sidebar, “The journey toward sophisticated risk-rating models”).

As the modus operandi for money launderers becomes more sophisticated and their crimes more costly, financial institutions must fight back with innovative countermeasures. Among the most effective weapons available are advanced risk-rating models. These more accurately flag suspicious actors and activities, applying machine learning and statistical analysis to better-quality data and dynamic profiles of customers and their behavior. Such models can dramatically reduce false positives and enable the concentration of resources where they will have the greatest AML effect. Financial institutions undertaking to develop these models to maturity will need to devote the time and resources needed for an effort of one to three years, depending on each institution’s starting point. However, this is a journey that most institutions and their employees will be keen to embark upon, given that it will make it harder for criminals to launder money.

About the author(s)

Daniel Mikkelsen is a senior partner in McKinsey’s London office, Azra Pravdic is an associate partner in the Brussels office, and Bryan Richardson is a senior expert in the Vancouver office.



Source:https://www.mckinsey.com/business-functions/risk/our-insights/flushing-out-the-money-launderers-with-better-customer-risk-rating-models?

AML-Scotiabank’s chief risk officer on the state of anti–money laundering,by Erez Eizenman

The following information is used for educational purposes only.


Scotiabank’s chief risk officer on the state of anti–money laundering

October 2019 | Interview

By Erez Eizenman


Daniel Moore talks about finding bad guys, creating good money, and everything in between.

Twenty years ago, anti–money laundering (AML) was an afterthought for most banks. Today, it’s at or near the top of the executive agenda. Daniel Moore is group head and chief risk officer at Scotiabank, one of Canada’s top five banks, with 99,000 employees and more than $1 trillion in assets. Recently, McKinsey’s Erez Eizenman spoke with him in Toronto about Scotiabank’s efforts to combat financial crime. An edited transcript of their conversation follows.

McKinsey: As chief risk officer, it’s your job to stay awake at night worrying about various risks. Where does money laundering rank?

Daniel Moore: I think the biggest challenge for banks these days is strategy and brand. There’s a lot happening on various fronts: regulation, competition, data, and technology. And in a low-rate environment, margins are challenged. But our main concern is to understand our industry’s competitive advantage, embrace it, and enhance it. One such advantage is customer trust. We have that today, and we need to value it. Customer trust derives from brand. AML, which is really about ensuring responsibility in our banking capacities, is critically important to upholding the value of brand and enhancing customer trust. So getting AML right is of critical strategic importance to our bank.

McKinsey: How is the industry doing at maintaining that customer trust and managing the money-laundering risk?

Daniel Moore: The industry is on the early part of that arc. Even though banking has worked at this for years, it takes a long time to move beyond regulatory compliance and into effectiveness. That’s the journey the industry is on: discovering the abilities of data and technology to get to effective outcomes, as opposed to regulatory compliance. We see this in the headlines every day. We are still focused on regulatory compliance.

It’s critical to understand that the landscape is changing on two frontiers. One is the regulatory frontier, and the other is the environment in which we operate. We talk often about how the bad guys change how they operate every single day. And they are as sophisticated as banks, make no mistake. But the regulatory environment is also changing. Keeping pace with both effectively isn’t always easy; sometimes you need to decide which you want to pay more attention to.

McKinsey: How have you managed that at Scotiabank?

Daniel Moore: It’s always a balancing act. There’s no right answer. Knowing your regulator well, establishing a relationship, and ultimately aligning your interests are of critical importance. It’s also important to have really good governance. That’s something we’ve paid particular attention to in the last year or so. In big enterprise initiatives, it’s easy to move quickly to the tactical. And the tactical becomes disorganized. So effective governance, to make sure you’re focused on the right things at the right times, is important for an effective AML program.

McKinsey: For many banks, managing that balance means moving beyond all the manual work required in due diligence to using technology and analytics. Was that true for you?

Daniel Moore: Yes. Analytics is probably one of the most overused terms right now because it can mean so much. Analytics for AML can range from very simple, linear rules all the way to backward-propagated neural-network models. We use all of those—and everything in between—because there’s yield from each one. Part of the challenge is to make sure you’re using the appropriate tool at the right time for the appropriate outcome. Everyone wants to use the most sophisticated, complicated tool all the time. That isn’t always the most effective choice—nor the most explainable or acceptable from a regulatory perspective. But let’s be clear: for everything from name screening to transactional monitoring, we have not found any part of our AML program that hasn’t been positively and materially affected by the use of analytics.

McKinsey: What are your guidelines for applying analytics to AML?




Impact through analytics

Daniel Moore: The key observation is that, sometimes, effectiveness can derive from very simple outcomes, very simple rules, very simple filters. And it’s important to think about where and when you apply those tools. I come back to Ajay Agrawal’s paradigm of the simple economics of analytics. Analytics has made prediction very cheap, but it doesn’t mitigate the need for the kind of judgment in which people review outcomes. We can modify the filters and the funnel that go into a judgment, making it more effective. We can also enhance the tools used to make a judgment more productive. But ultimately, we still need that third level of judgment in which we look at cases and outcomes. That will remain expensive. But as prediction becomes even more widely applied and cheaper, the judgment will become more productive.

McKinsey: What are the technical challenges of setting up that kind of ideal, in which judgment sits atop machine models?

Daniel Moore: We’ve had two big challenges. One is sourcing the data. Most banks deal with multiple legacy systems holding data in many places. And producing an integrated data schema from that, where you can look at data effectively, is challenging. It’s not beyond the wit of man, but it’s a big piece of work to get right.

The other is what we refer to as the “IP [intellectual property] of AML judgment.” It is knowing what you’re solving for. Many of today’s high-profile cases would have been compliant with yesterday’s rules. So knowing about regulatory change, knowing what to look for in your systems to produce effective outcomes, is critically important. That’s an ongoing education.

McKinsey: We’d love to understand what you think about the future of analytics in AML.




Better outcomes through industry collaboration

Daniel Moore: The challenge is that we’re not only looking for a needle in a haystack, we’re looking for a needle in a stack of needles. And we don’t even know if we have the whole stack of needles when we’re doing it. So in the future, collaboration will be vital: across the financial-services industry, government, and law enforcement. The ability to put together our data sets and collaborate on typologies of attack—and the use of both advanced-encryption methods and analytics methods to mine the data—will enhance yields by orders of magnitude. That’s the ultimate direction. Some jurisdictions are further ahead than others. But I think all are moving in this direction. And ultimately, that comprehensive, 360-degree view will produce better outcomes for all stakeholders.

McKinsey: Let’s talk about the regulatory side of the balance you mentioned. Explaining your new uses of analytics could be a difficult conversation to have with a regulator.

Daniel Moore: Ultimately, it’s about understanding that the regulator’s objectives are aligned with our objectives. Simply put, that’s to find bad guys inside our system. We both want to achieve the same thing. So how do we enhance that alignment of interests? Communication and relationships are important in whatever jurisdiction you’re operating in—relationships with the regulator, bringing them along on the journey. In many jurisdictions, including the US, we’ve seen a shift in regulatory expectations where they are more open to a focus on the use of analytics to produce better outcomes.

McKinsey: Have you educated the regulator as you go?

Daniel Moore: It behooves us as an industry, because we are at the “coal face” of analytics, to educate the regulator. We’ve also found that the regulators are highly interested in learning and taking this journey alongside us. And that makes for effective challenge and governance on what we produce.

McKinsey: How do you think about metrics and tracking, both internally and to share with the regulator?

Daniel Moore: Like any big initiative, there are several metrics that can help, starting with production metrics in AML operations. In technology, we look at effectiveness, efficiency, and coverage metrics. We also have KPIs [key performance indicators] for a wide variety of outputs and backlogs. But ultimately, coming back to our objective, what it comes down to is risk appetite and our key risk indicators [KRIs]. Are we making progress against our risk-appetite metrics? Every form of risk, including AML, should have KRIs to assess the inherent risk, the mitigators, and the residual risk.

McKinsey: Big transformations need metrics and people to keep them on track. How critical is talent as part of that equation?

Daniel Moore: It’s probably obvious that talent is critical to the outcome. But talent isn’t just smart people. We have lots of smart people. Talent means people who have been on this journey and know the common pitfalls and can help you avoid them. The industry has been working on AML for many years now, so talent is available.

Some of those pitfalls are in data science. Historically, it’s been difficult to find data scientists. But the supply is increasing as universities and other organizations and even industry are training more people. The real challenge is finding people who understand both the data science and the business need. That’s pure gold—and rare.

McKinsey: Once you find the right people, how do you set them up to be successful?




Achieving cultural change

Daniel Moore: That’s really a question of organizational alignment or culture. When a data scientist meets with a business partner, will they find engagement or resistance? And the question then is, how important is AML to an organization? Because we see AML as intrinsically linked to brand, we believe it’s of fundamental importance to the organization.

McKinsey: No matter how large your AML team grows to be, there’s always a requirement for AML to be truly owned by the front line. How do you both educate the front line and instill in them that sense of ownership?

Daniel Moore: Many organizations, and we are not immune, start big risk initiatives within the risk group. And maybe that’s an OK place to start. But you’ll never be long-term successful if the risk is not owned by the first line of defense. It’s important to create accountability, so the first line feels like it owns—and does in fact own—the risk. Governance, challenge, and oversight come from the second line.

It’s an important point that cannot be underscored enough. We need to know our customers and understand that the capacities we’ve created, which are extraordinary and highly efficient and highly tuned, are used for the betterment of society, its communities, and its individuals. We call that “good money,” and we make sure that good money is what flows over our counters every single day.

McKinsey: How has that concept resonated in your bank?

Daniel Moore: If you asked me ten years ago, when I was in wholesale banking, whether I would be excited about being involved in AML, the answer would have been a resounding “no.” It was a paper exercise. It was a compliance exercise. But when you shift your perspective and realize that every bank today is faced with people who want to exploit it to conduct criminal enterprises, terrorism, human trafficking, you know that’s not the sort of bank—or the sort of industry—that you want to be part of. When you make it real in that way, people wake up and realize, “We are not going to walk by that standard.” Because the standard that you walk by is a standard that you accept.

The real challenge is finding people who understand both the data science and the business need. That’s pure gold—and rare.

McKinsey: That’s a compelling change story. Our research shows that the number-one reason a transformation fails is that the top leadership team doesn’t offer a convincing story of why change is needed.1 How important has that story been for Scotiabank?

Daniel Moore: The board, the CEO, the operating committee—they are all highly engaged on our AML journey and understand its importance to the bank, why it matters for us to be responsible bankers, and why it matters to the commercial enterprise.

McKinsey: What did you do to ensure that everyone in the bank heard that tone from the top?

Daniel Moore: There’s no one silver bullet. It’s like any other cultural change. It will take time. And it requires a variety of modalities to get it right: regular memoranda, emails, frequent mentions in town halls. Any forum where you can mention at least seven times the importance of what you’re after will bring that message home. We made some powerful videos that resonated throughout the organization. We brought in victims of human trafficking to speak to our bankers to help them understand what this means and how this is happening in our own backyard. Human trafficking is the fastest-growing form of crime in AML today. It’s a real tragedy in the cities in which we operate. It’s a stark message. But once you get it out there, people really lean into the outcome.

The communications make it real, moving it off the piece of paper with the checklist and into the “why” of what we’re doing. That’s true also of the regulatory direction in which we’re heading and the way we operate inside the bank. Simon Sinek talks about starting with “why.” That’s the core of what we do. And landing that is of critical importance.

McKinsey: What role does the board play in this?

Daniel Moore: AML is a significant expenditure of calories. It takes a lot of investment to get it right. You absolutely need the board’s high-level engagement, as we’ve had, to make sure you’re focused on getting it right and that you have the resources available to deploy against that outcome.

McKinsey: Do you view AML as a source of competitive advantage?




AML as a competitive advantage

Daniel Moore: Yes. An effective AML program will be a competitive advantage, not simply because of what it does to enhance the brand and build trust, but also because it allows you to do what you do more effectively. The consequences of getting it wrong are vast. A bank that falls down on AML might lose 20,000 commercial customers in a month. That’s because environmental, social, and governance issues matter more today than ever.

But the core of AML is relationships: knowing your customers better and being able to take smart risks of every kind when the bank underwrites a customer. Banks have a charter and a mandate in the communities and societies in which they operate to create capital for those that will put it to responsible uses.

Understanding our customers better, a better ability to rate risks, and intelligence about where we’re deploying our capital will allow the industry to responsibly deploy capital with those that need it, which is valuable to the communities in which we operate and to the banks that are able to operate safely in those jurisdictions. That’s what we’re working on.

About the author(s)

Erez Eizenman is a partner in McKinsey’s Toronto office. Daniel Moore is group head and chief risk officer at Scotiabank.


Source:https://www.mckinsey.com/business-functions/risk/our-insights/scotiabanks-chief-risk-officer-on-the-state-of-anti-money-laundering?