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World banking Annual Review 2019: Need for Urgent Steps in the Late-cycle 

 Signs that the banking industry has entered the late phase of the economic cycle, a decade on from the global financial crisis, are clear. Growth in volumes and top-line revenues are slowing, with loan growth of just 4 percent in 2018—the lowest in the past five years and a good 150 basis points (bps) below nominal GDP growth Yield curves are also flattening. And although valuations fluctuate, investor confidence in banks is weakening once again. 

The Global Return on Tangible Equity

These cycles are not new to seasoned industry experts. Global Return On Tangible Equity (ROTE) has flat lined at 10.5 percent, despite a small rise in rates. Emerging-market banks have seen ROTEs decline drastically, from 20.0 percent in 2013 to 14.1 percent in 2018, largely due to digital disruption that continues unabated. Banks in developed markets have strengthened productivity and managed risk costs, lifting ROTE from 6.8 percent to 8.9 percent. But on balance, the global industry approaches the end of the cycle in less than ideal health, with nearly 60 percent of banks printing returns below the cost of equity. A prolonged economic slowdown with low or even negative interest rates could wreak further havoc.

What explains the difference between the 40 percent of banks that create value and the 60 percent that destroy it are geography, scale, differentiation, and business model.

It can be realised that the domicile of a bank explains nearly 70 percent of underlying valuations. Consider the United States where banks earn nearly ten percentage points more in returns than European banks do, implying starkly different environments. 

Global risk costs are at an all-time low, with developed-market impairments at just 12 bps. But just as counter-cyclicality has gained prominence on regulators’ agendas, banks also need to renew their focus on risk management, especially the new risks of an increasingly digital world. Advanced analytics and artificial intelligence are already producing new and highly effective risk tools; banks should adopt them and build new ones. On productivity, marginal cost-reduction programs have started to lose steam. The present need is to industrialize tasks that don’t convey a competitive advantage and transfer them to multi-tenant utilities. Industrializing regulatory and compliance activities alone could lift ROTE by 60 to 100 bps. Last but not least, on generating elusive revenue growth, now is the time to pick a few areas—client segments or products—and rapidly reallocate top customer-experience talent to attack the most valuable areas of growth and take share as competitors withdraw and customer churn increases late in the cycle.

Making the right moves for the right bank

All banks are not the same. The level of strategic freedom every bank enjoys depends on its business model, assets, and capabilities relative to peers as well as on the stability of the market in which it operates.

The Four Archetypes

Each bank can be classified into one of four archetypes, each with a set of levers that management should consider. These archetypal levers form a full picture of the degrees of freedom available to a bank. These are defined by two dimensions: the bank’s strength relative to peers and the market stability of the domain within which the bank operates.

World Bank B

 

  • Market leaders: Consider them as the top-performing financial institutions in attractive markets. 
  • Resilients: These tend to be top-performing operators that generate economic profit despite challenging market and business conditions.
  • Followers: Tend to be midtier organizations that continue to generate acceptable returns, largely due to the favourable conditions of the markets in which they operate, but whose overall enterprise-strength relative to peers is weak. 
  • Challenged banks: These banks generate low returns in unattractive markets and, if public, trade at significant discounts to book value. 

Strategies of Archetypal Levers

Archetypal levers comprise three critical moves—ecosystems, innovation, and zero-based budgeting (ZBB).  Combining the universal and archetypal levers results in the degrees of freedom available to each bank archetype. 

World Bank C

 

Market leaders: Priorities to retain leadership into the next cycle

Market leaders have benefited from favourable market dynamics as well as their (generally) large scale, both of which have allowed them to achieve the highest ROTEs of all bank archetypes—approximately 17 percent average ROTE over the previous three years. And they have achieved this leadership without having to focus too much on improving productivity, as reflected in their average cost-to-asset ratio (C/A) of approximately 220 bps. Whereas most of the market leaders in developed markets are North American banks, a significant proportion (approximately 46 percent) of market leaders consists of banks in emerging markets in Asia—mainly China—and the Middle East. These banks, even with declining ROTEs in the previous cycle, still have returns above the cost of capital.

Goals for the late-cycle

These banks must understand their key differentiating assets and invest in innovation, using their superior economics especially when peers cut spending as the late-cycle bites. As noted earlier, history shows us that approximately 43 percent of current leaders will cease to be at the top come the next cycle. The investments made now—whether organic or inorganic—will decide their place at the top table in the next cycle.

Given the scale advantages that leaders enjoy, banks in this group will be challenged to sustain revenue growth, especially as credit uptake typically slows in the late-cycle. The focus now needs to shift toward increasing their share of wallet among current customers by extending their proposition beyond traditional banking products.

 

Resilients: The challenge of managing returns in sluggish markets

Resilients are almost all in Western Europe and developed Asian markets such as Japan, which have been the toughest banking markets over the past three years. Leading broker-dealers also feature in this group.

They are strong operators and risk managers that have made the most of their scale in what have been challenging markets, due to either macroeconomic conditions or to disruption. This has allowed them to generate returns above the cost of equity, with an average ROTE of 10.7 percent over the previous three years, without taking on undue risk, as reflected in the lowest impairment rates of all archetypes (24 bps). Banks in this archetype have worked hard at costs even as they have struggled to maintain revenues, beating the C/A ratios of market leaders (their peers in buoyant markets) by nearly 50 bps. However, at 170 bps, there is still significant opportunity for productivity improvements when compared with best-in-class peers. 

Goals for the late-cycle

The first item on their agenda, just like market leaders, should be to focus on increasing their share of wallet among their current customers through enhanced customer experience (CX) and by building a value proposition that extends beyond the traditional set of banking products. Those with a large infrastructure asset (for example, securities companies) should innovate by their platforms across noncompeting peers and other industry participants to find new ways of monetizing their assets. On the cost front, resilients need to pay closer attention to opportunities for improving productivity by exploring the bank-wide appetite for ZBB. They should remain alert to the possibility of a compelling distressed asset becoming available.

 

Followers: Preparing for tailwinds turning to headwinds

Approximately 76 percent of followers are North American and Chinese banks. Followers are primarily midsize banks that have been able to earn acceptable returns, largely due to favourable market dynamics. However, their returns (on average 9.6 percent ROTE) have been a little more than half of those of market leaders, who have also operated with the same favourable market dynamics. The principal driver of their underperformance relative to market leaders is in revenue yields, where they are 100 bps lower. Again, given their underperformance relative to other banks in similar markets, they have invested in productivity improvements and have C/A ratios 20 bps lower than market leaders but 70 bps higher than similarly underperforming peers in more challenged markets.

Goals for the late-cycle

A clear need for action is needed with bold moves to ensure that returns do not deteriorate materially during a downturn. Furthermore, if they are to be among the 37 percent of follower banks that become leaders regardless of the market environment, now is the time to build the foundation, as they still have time to benefit from the excess capital that operating in a favourable market gives them.

Given their subscale operations and the fact that they are still in a favourable market, they should look for ways to grow scale and revenues within the core markets and customer sets that they serve.  Cost is also a significant lever for this group. With an average C/A ratio that is 70 bps higher than peers in more challenged markets (where challenged banks as a group have pulled the cost lever harder than other archetypes), followers have the potential to improve productivity significantly.

 

The challenged: Final call for action

Some 36 percent of banks globally have earned a mere average of 1.6 percent ROTE over the past three years. This is the lowest average return of all archetypes and well below the cost of equity of these banks. With an average C/A ratio of 130 bps, they have the best cost performance. However, the problem is in revenues, where they have the lowest revenue yields, at just 180 bps, as compared with an average revenue yield of 420 bps among market leaders. Further analysis of this category also points to the fact that most operate below scale and are “caught in the middle,” with neither high single-digit market share nor any niche propositions. Most of these banks are in Western Europe, where they compete with weak macro conditions (for example, slow loan growth and low interest rates).

Goals for the late-cycle

The sense of urgency for challenged banks is particularly acute given their weak earnings and capital position; banks in this group need to radically rethink their business models. If they are to survive, they will need to gain scale quickly within the markets they currently serve.

Thus, exploring opportunities to merge with banks in a similar position would be the shortest path to achieving that goal. 

The only other lever at hand costs, in which this group already leads other banks. However, there should still be further opportunities, including the outsourcing of non-differentiated activities and the adoption of ZBB, both discussed earlier. With an average C/A ratio of 130 bps, challenged banks as a group still have a good 50 bps to cover before they produce the best-in-class cost bases we’ve seen from Nordic banks.

As the current market uncertainty generates concerns for market players, one question that rests heavily on the minds and lips of stakeholders is ‘what will result in an imminent recession or a prolonged period of slow growth?’ As growth slows and is unlikely to quicken in the medium term, there is no doubt that this is the era of the late cycle. Aggravating this situation is the continued threat posed by fintechs and big technology companies, as they take stakes in banking businesses. Whether a market player or an underdog, the time for bold and critical moves is now.

NEW STRATEGIES TO COMBATING MONEY LAUNDERING

NEW STRATEGIES TO COMBATING MONEY LAUNDERING

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. 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. 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. Some of those leaders have adopted the approach to customer risk rating described in this narration, 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.

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. 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.

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 sub-segments 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.

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.

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.

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.

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. 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.

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.

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.

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