Transform Your Business With Operational Decision Automation

Decisioning Applications Bring The Value Of Operational Decisions To Light

Businesses face the imperative to transform business from analog to digital due to intense competition for increasingly demanding and digitally connected customers. The imperative to transform has ushered in a new era of decisioning applications in which every operational decision an organization makes can be considered a business asset. New applications inform and advance customer experience and drive operational actions in real time through automation. These applications are at the forefront of the effort to streamline operations and help organizations take the right action at the right time near-instantaneously.

Achieve Digital Goals With Automated Operational Decision Making

Automating decision life cycles allows firms to manage the fast changes required in increasingly digitized business processes. Automation of operational decisions is crucial to meeting digital goals: More than three-quarters of decision makers say it is important to their digital strategy —and close to half say it is very important.

« The Share of Decisions that are Automated will Increase Markedly in two Years »

The importance of automated operational decision making to digital strategy will lead to a sharp increase of automation in the near term. Today, about one-third of respondents say they have the majority of their operational decisions fully or partially automated. In two years, that group will double.

Use Cases For Automated Decisions Span The Customer Lifecycle But Current Focus Is On Early Stages

To improve the operational aspects of customer experience —and to reap the business benefits that come with delighting customers — firms align automated decision use cases to the customer lifecycle. At least some firms have expanded their share of automated operational decision making to include touchpoints across the customer lifecycle, from the discover phase all the way to the engage phase. However, our survey found that the majority have yet to implement automated decisions as fully in later stages.

Top Challenges Will Intensify With Rapid Expansion Of New Decisioning Tools

Firms are experiencing middling success with current decision automation tools. Only 22% are very satisfied with their decisioning software today. Misgivings with today’s tools include inability to integrate with current systems or platforms, high cost, and lack of consistency across channels and processes.

The growth of real-time automation use cases and the number of technologies brought on to handle them will exacerbate existing challenges with complexity and cost.

Decision Makers Recognize High Value In Decisioning Platforms That Work In Real Time

Decision makers face significant implementation and cost challenges on their path to automated operational decisions. As a result, getting the greatest business value for the power of their automation tools is top of mind.

« Eighty-one percent of Decision Makers say a Platform with Real-Time Decision-to-Action Cycles would be Valuable or Very Valuable to Achieving Digital Transformation Goals. »

With better, targeted decisions based on real-time analytics, companies have the potential to acquire better customers, improve the operations that serve them, and retain them longer.

decision automatization

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Banks sailing in uncertain waters

The decision-making process apparent paradox

Corporate decision-making processes are driven by seemingly opposing forces.

On the  one hand, the human urge to dispose of instruments emerges in order

  • to understand context, specific self-direction
  • and to implement the actions required for following the plotted course.

On the other hand, the exhortation to keep the mind open

  • to an array of possible future scenarios,
  • to imagine and grasp the implications of the various possible trajectories,
  • to plot alternative courses according to the obstacles and opportunities encountered, that could lead to landing places other than those contemplated originally.

Needs that are intertwined as never before whenever the decision-maker operates in an area such as the banking sector, that is characterised by extremely pervasive regulatory requirements concerning the

  • maintenance and use of capital,
  • liquidity management,
  • checks on lending and distribution policies,

and that is structurally exposed to the volatility of the macroeconomic context and financial markets, greatly increasing the range of possible scenarios.

Thus, it is far from surprising or infrequent that one of the most common questions that CEOs ask the technical structures responsible for budgeting and risk planning is: ‘what if’? (‘what would happen if…?’). The problem is that, in the last few years, the ‘ifs’ at hand have rapidly multiplied, as there has been an exponential increase in the controlling variables for which feedback is required:

  • Net Interest Income (NII);
  • Cost Income ratio (C/I);
  • Return On Equity (ROE);
  • Non Performing Exposure (NPE) Ratio;
  • Liquidity Coverage Ratio (LCR);
  • Expected Credit Loss (ECL);
  • Common Equity Tier 1 (CET1) ratio,

to cite but a few among the most widespread. Planning has turned into an interdisciplinary and convoluted exercise, an issue hard to solve for CFOs and CROs in particular (naturally, should they not operate in close cooperation).

This greater complexity can result in the progressive loss of quality of the banks’ decision-making process, more often than not based on an incomplete information framework, whenever some controlling variables are unavailable, or even incorrect when there is an actual lack of information, specialist expertise and/or the instruments required for the modelling of events.

Partial mitigating circumstances include the fact that such events, aside from being numerous, are interdependent in their impact on the bank’s results and are particularly heterogeneous. These can in fact be exogenous (turbulence and interference along the way) or endogenous (the actions that the helmsman and the crew implement during navigation).

In the first case, these events are beyond the control of those responsible for the decision-making process, determined by the evolution of the market conditions and/or the choices of institutional subjects. As such, they are often hard to predict in their likelihood of occurrence, intensity, timing and duration. By nature, such phenomena are characterised by complex interactions, that make it crucial, albeit arduous, to comprehend the cause-effect mechanisms governing them. Lastly, their relevance is not absolute, but relative, in that it depends on the degree of reactivity of the bank’s business model and budgetary structure to the single risk factors to which the market value of the banks’ assets is exposed.

Conversely, in the case of endogenous events, uncertainty is more correlated to the ability of the bank’s top management

  • to quantify the level of ambition of the business actions,
  • to assess their multiple implications,
  • and specifically, to the bank’s actual ability to implement them within requested time frames and terms.

The taxonomy of banking strategic planning

Although these complexities are increasingly obvious, many banks still remain convinced about getting started on their respective courses with certainty, exposing themselves to a range of risks that can restrict or irreversibly compromise the efficacy of the decision-making processes. Some institutions are indeed persuaded that an ‘expert-based’ approach that has always characterised their planning methodologies shall continue to be sufficient and appropriate for steering the bank, also in future.

History teaches us that things have not always worked out that way. These actors have yet to understand that it has now become vital to foster the evolution of the planning process towards a model relying upon analytical methodologies and highly sophisticated and technological instruments (risk management, econometrics, statistics, financial engineering, …), making them available to those that have always considered experience, business knowledge and budgetary dynamics to be privileged instruments for making decisions.

Second mistake: many banks believe the uncertainty analysis to be wasteful and redundant for the purposes of planning since, ultimately, the allocation of objectives is (and will remain) based on assumptions and uniquely identified scenarios. In this case, the risk lies in failing to understand that, in actual fact, a broader analysis of possible scenarios contributes to better delineating the assigned objectives, by separating the external conditions from the contribution provided by internal actions. Moreover, testing various hypotheses and combinations of cases makes it easier to calibrate the ‘level of managerial ambition’, in line with the actual potential of the organisational structure and with the full involvement of the business functions responsible for attaining the corporate objectives.

The intersection of these two misreadings of the context results in a different positioning of the bank, with the relative risks and opportunities.

Models

ILLUMINATED

The planning process is built upon analytical data and models developed with the contribution of subject matter experts of different origins, which allows to consider the impacts of a specific scenario on the bank’s budget simultaneously and coherently. Nevertheless, not only does it improve the planning of a specific item, but it disposes of appropriate instruments to switch to a multi-scenario perspective and investigate the relevant scenarios for management, appraising the volatility regarding the expected results. This transition is extremely delicate: it entails a change in the way prospective information is produced by the technical functions and subsequently channelled to the top management and board of directors. In this context, the bank is governed via the analysis of deterministic scenarios and the statistical analysis of the probability distributions of the variables of interest. Leveraging this set of information (much more abundant and articulated than the traditional one) targets, risk propensity levels and relative alert and tolerance thresholds are established; business owners are provided not only with the final objectives, but also with details concerning the key risk factors (endogenous and exogenous alike) that might represent critical or success factors and the respective probabilities of occurrence.

DELUDED

The budget planning process is characterised by the prevalence of an expert-based approach (with a limited capacity of integrating quantitative models and methodologies, in that not always all budget items are developed by relying on the necessary instruments and expertise) and aimed at forecasting a single baseline scenario (the one under which the budget objectives are to be formalised, then articulated on the organisational units and business combinations).

ENLIGHTENED

The budgetary planning process is very accurate and incorporates specialist expertise (often cross-functional) required to understand and transmit the interactions across the managerial phenomena so as to ensure a full grasp of the bank’s ongoing context. The onus is chiefly on the ability to explain the phenomena inside the bank without prejudice to the external baseline scenario, that is ‘given’ by definition.

MISSING

The planning process attempts to consider the impact of alternative scenarios as compared to the baseline scenario, however, it is implemented on the basis of imprecise or incomplete modelling, in that developed without the analytical foundations and instruments required to appraise the consistency and the degree of likelihood of these scenarios, useful tools to sustain such a serious consideration. The focus remains on the comparison across the results produced under diverse conditions, while taking into account the approximations used.

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