Transforming Financial Institutions

Value Creation through Technology Innovation and Operational Change

Joerg Ruetschi

• Publisher: Wiley
• Year: 2022
• Print ISBN:
  9781119858836
• Edition: 1
• eBook ISBN:
  9781119858850
• Topic: Business
• Subtopic: Corporate Finance
• Index: Business

Transform your financial organisation's formula for value creation with this insightful and strategic approach 

In Transforming Financial Institutions through Technology Innovation and Operational Change, visionary turnaround leader Joerg Ruetschi delivers a practical and globally relevant methodology and framework for value creation at financial institutions. The author demonstrates how financial organisations can combine finance strategy with asset-liability and technology management to differentiate their services and gain competitive advantage in a ferocious industry. 

In addition to exploring the four critical areas of strategic and competitive transformation — financial analysis, valuation, modeling, and stress — the book includes: 

• Explanations of how to apply the managerial fundamentals discussed in the book in the real world, with descriptions of the principles for reorganization, wind-down and overall value creation 
• An analysis of the four key emerging technologies in the financial industry: AI, blockchain, software, and infrastructure solutions, and their transformational impact 
• Real-world case studies and examples on how financial institutions can be repositioned and rebuilt on a path of profitability 

Perfect for managers and decision makers in the financial services industry, Transforming Financial Institutions through Technology Innovation and Operational Change is also required reading for regulators, tech firms, and private equity and venture capital funds. 

PART One
Managerial Fundamentals

The first part introduces the fundamentals concepts and managerial fundamentals that drive decision making and operational excellence in financial services. These building blocks cover strategic and financial decision making, asset‐liability management as well as technology management and innovation. The first two chapters frame the decision‐making methodology for financial institutions and the later chapters discuss with balance‐sheet strength and technology management two key capabilities of a successful financial‐institution model.

CHAPTER 1
Strategic Decision Making

Strategy is a very broadly used term applied in military, political, and business situations. Lawrence Freedman illustrates in his book Strategy—A History the origin of strategy though mythical figures, politicians, and business leaders. They all have in common to take decisions under uncertainty and confusion of the human affair. Strategic decision making aims to clarify these moments of uncertainty with an applied set of problem‐solving principles. It is therefore a problem‐solving methodology that is applied to complex, unstructured, and multidimensional issues, driven by the uncertainty of human behaviour and decision making. Max McKeown specifies in The Strategy Book that strategy is about shaping the future by which people attain desirable ends with available means. Authors such as Igor Ansoff, Henry Mintzberg, and Michael Porter established during the last 50–60 years a corporate discipline that aims to combine a rigorous planning approach with adaptability to the circumstances given the uncertainty of the situation.

Corporate and business strategy follows a vision for a business in accordance with an executable mission of a specific commercial and operational plan. This involves determining and implementing long‐term goals and objectives, with the considerations of a financial institution's market positioning and available capabilities. These considerations include competitive analysis, macroeconomic and industry conditions, regulatory constraints, and other dimensions of the internal organisation and external environment. The following chapter introduces a series of core strategic principles and applies them to financial, commercial, and operational problem solving in financial institutions. It covers tools of strategic analysis such as hypothesis‐driven problem solving, system theory and coherence, as well as strategic planning such as roadmaps; heat maps; strengths, weaknesses, opportunities, and threats (SWOT); and scenario analysis. It further discusses the operational efficiency and the design of target operating models (TOM), performance improvement, and merger & acquisition (M&A) as fundamental tools of the strategic decision‐making process. The ability of an institution to convert strategy into a success business is directly related to the continuous testing of the logic and the assumptions when making structured decisions about how to win in a competitive marketplace.

1.1 STRATEGIC ANALYSIS

Strategic decision making develops a systematic approach and defines a methodology to analyse business situations and solve entrepreneurial issues that are by nature complex and unstructured. It solves the key question on how to get a competitive advantage over the opponent in resolving the issues. The complex nature and dynamic relationship of the underlying drivers require a systematic problem‐solving mindset. In business and finance, this problem‐solving mindset establishes a clear understanding of the dynamics of the market positioning of a business with its revenue stream and the effectiveness of the operating platform with its cost structure. System theory is used to further assess the dynamics within the assumptions and limitations of theoretical models. At the same time, every strategy has to be coherent with an organisation's market positioning, capability set, and product and services fit while following a best‐in‐class benchmark. The following section introduces those three tools of strategic analysis.

1.1.1 Hypothesis‐driven problem solving

Strategic analysis is by nature solution oriented and hypothesis driven while being fact‐based at the same time. The process of coming up with a good strategy has the same logical structure as the problem of coming up with a good scientific hypothesis, as strategy deals with the edge between the known and the unknown. It is an educated guess about what will work in a specific situation. As a decision process, it applies a structured framework to generate fact‐based hypotheses followed by data and information gathering with the objective to prove or disprove the hypotheses.

This decision and problem‐solving process is therefore often referred to as a hypothesis‐driven approach. The hypothesis‐driven approach is a cornerstone in strategic analysis. It starts with understanding the situation, framing the problem, analysing through gathering information as well as data, and finally concluding the analysis by guiding policies and coherent action plans. In its analytical approach, it simplifies the often overwhelming complexity and reality by identifying certain aspects of the situation as critical.

This hypothesis‐driven analysis is driven by three core principles that can be summarised as follows:

1. Simplify and reduce complexity by stating early an initial hypothesis which leads to a result‐driven analysis.
2. Specify and focus through identifying key drivers in a designated analytical framework that is mutually exclusive collectively exhaustive (MECE).
3. Quantify and validate through available information and related data.

As Exhibit 1.1. illustrates, every strategic issue, as any complex und unstructured problem‐solving challenge, starts with developing and testing a bunch of hypotheses that sets out the path how a situation can be resolved. This path knows five major steps.

The principles of the hypothesis‐driven approach provide a central decision platform, based on which efficient problem‐solving process can be applied. The process is defined through a structured framework that is centred on the initial hypothesis. It begins with framing the problem through understanding the situation and defining the boundaries of the analysis. It early comes up with an initial hypothesis and further breaks down the issue in its core components. By gathering information and data and through analysing them, it is continuously refined until a synthesis and conclusion can be reached. There are five steps that further define this methodology.

Exhibit 1.1 Hypothesis‐driven problem solving¹

Baseline, structure, and hypotheses

Step 1: Understand the situation and assess the problem

The objective here is to fundamentally comprehend the situation of a business by looking into initially available information and data. The step is known as baselining as it organises available data in a format that then further drives the structure of the analytical framework and the solution path around it.

In a restructuring situation, the incoming management team assesses the situation with readily available information while starting to work on a more detailed baselining. The baselining provides a detailed description of the business model, outlines the target operating model with its value chain, and provides a functional breakdown of the operational cost base.

Step 2: Frame the problem through hypotheses and structure a framework

In a first diagnosis, a clearly defined framework allows the management team to come up with an initial hypothesis on how to resolve the problem directionally. It is shaped across the core elements of the situation and its related issues. The following key elements are to be considered:

• – Set up the analytical framework through defining the issues in a structured approach with its key drivers and m...

Table of contents

• Cover
• Table of Contents
• Title Page
• Copyright
• Dedication
• About the Author
• Introduction
• PART One: Managerial Fundamentals
• PART Two: Repositioning Financial Institutions
• PART Three: Conclusion
• Afterword and Acknowledgment
• Bibliography
• Index
• End User License Agreement