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Analytical Thinking

Analytical Thinking

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Welcome to Analytical Thinking

This lesson will introduce you to the concepts behind analytical thinking. You will learn what the core elements are and how this way of thinking can enhance defining problems and determining solutions. You will also gain insight into how reflection can refine your analytical reasoning and view some examples of frameworks that can improve your decision-making ability.

This training will take approximately 25 minutes to complete.

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Lesson Learning Objectives

After completing this lesson, you will be able to:

  • Identify key components of analytical thinking.
  • Explore the frameworks to approach work in a more analytical manner.
  • Demonstrate how evidence-based and well thought reasoning can improve analysis and decision making.
  • Gather, interpret, and evaluate data with an open mind and awareness of bias.
  • Demonstrate how analytical thinking frameworks can be leveraged to deconstruct complex problems and identify solutions.

Completion Criteria

This lesson contains a final assessment. You must score 80% or higher on the assessment to complete this lesson.

You have already met the requirements for completing this lesson. You may now review content or exit the lesson.

This lesson also includes an optional test-out. If you pass, you can bypass the lesson content and final assessment and exit the lesson.

If you prefer, you can skip the test-out and go straight to the content.

The Core Elements of Analytical Thinking

The Core Elements of Analytical Thinking

The core of analytical thinking involves systemically evaluating information. It requires you to gather facts, interpret what they mean, and make better decisions.

When you face complex challenges, such as an underperforming portfolio or an unexpected drop for a key metric, you define the problem, examine evidence and draw logical conclusions.

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The 4-step model


To successfully use analytical thinking, it helps to follow a structured approach when resolving a problem or decision making.

The following 4-step model can be used to walk through the process for every problem or every decision.

Define the Problem
Gather the Data
Analyze & Test
Conclude & Communicate

Define the Problem

Before identifying a solution, the problem must be defined. This requires asking the right question to pinpoint the core issue and help understand what needs to be resolved.

Digging deeper, and routing out the cause, we will need to ask questions such as: What is causing the underperformance, and what should we do about it?

To find this cause the expected performance and actual performance need to be identified. Upfront clarification prevents aimless data gathering and ensures there is focus on the actual problem. It also keeps things easy to understand and to communicate. Focused questions also ensure that there is less risk of sidetracking or distraction due to the volume of data.

What data and assumptions matter?

Gather the Data

Once the problem is identified, the next step is to gather relevant information.

This involves determining what data or evidence is needed to address the question and then collecting those facts. Any assumptions made must also be listed.

Gathering facts requires being thorough and focused. It’s easy to grab too much data (every tick of every stock) or not enough data (just the total return numbers). The key here is to optimize for relevant data.

A useful approach is to build categories to keep everything organized and structured. One analytical tool that can help with this is the Mutually Exclusive, Collectively Exhaustive (MECE) framework, which we will explore in Section 4: Categorization.

An important aspect of the data gathering step is the need to avoid opinions and biases. Try to gain multiple perspectives from different sources and, if relevant, listen to the views of various stakeholders. Once you have collected the information you need, you are ready to analyze it.

Analyze & Test

The next step of the process involves the analysis itself. Now we need to explore the data in order to discover patterns, causes and insights.

To proceed, select the arrow on the right to learn more.

 

Analysis

There are several analytical tools that we can use to do this effectively. We will take a closer look at these later in this lesson.

Effective analysis requires being skilled with critical thinking and overcoming biases. It is also important that the data can be categorized correctly so that it can be organized effectively.

It also requires identification of root causes for the problems we are trying to analyze. Along with this, we must be skilled at synthesizing and identifying key insights from the data.
 

Four Skills

These are the four skills we will cover later in this lesson:

  1. Critical Thinking
  2. Categorization
  3. Causal Analysis
  4. Collection and Synthesizing
 

Testing

Throughout the analysis, we should also test our thinking. We need to question our findings, seek evidence for or against them, and perhaps run small experiments.
 
 

Conclude & Communicate

Now that we have our insights, what is our next step?

While analysis helps us make sense of what’s happening, it is not enough to just understand the situation. We need to be able to draw conclusions and communicate those conclusions clearly and confidently.

When we identify the root cause and organize data correctly, we can synthesize what really matters and turn insights into action. Then we must present our recommendations clearly, concisely, and compellingly, whether to a client, a manager, or a committee.

When communicating, structure matters. One of the most effective approaches is the Bottom-Line Up Front method. People don’t want to dig for the insight; they want the main point straight away.

When presenting, clarity builds trust. Don’t hide your insight behind jargon or overly long explanations. Even if people can’t follow every detail of your analysis, with a clear and grounded message, your thinking will be trusted. Use bullet points or visuals to highlight your logic and lead with your headline recommendation, make it the first thing they hear or see.

Remember the Pareto Principle (the 80/20 rule). In analysis, 80% of the value often comes from 20% of the effort. Your goal is to identify the few factors that drive the greatest impact.

The 4-Steps in Practice

Let’s look at how the 4-steps apply in an actual scenario:

A client’s investment portfolio is underperforming compared to its benchmark. It appears that it has been underperforming the market index for three consecutive quarters.

To proceed, select each step to learn more.

Define the Problem
Gather the Data
Analyze & Test
Conclude
Communicate

Define the Problem

Taking our scenario, we need to set out and define the problem. What is discovered isn’t the problem, but it is a symptom. Asking some questions can help reveal the problem.

Some questions to ask include: ‘why is the portfolio underperforming and how can we fix it?’ or ‘How can we improve the portfolio’s performance next quarter?’

Taking our example again, we can now reframe it as: ‘Our client’s portfolio is trailing its benchmark by 3%. We need to determine why this is happening and what action will improve performance while aligning with the client’s goals.’

This explicitly states the gap (3% underperformance) and sets two tasks:

  • Diagnose the cause
  • Recommend an action

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Gather the Data

With our problem identified the next step is to gather the necessary data.

For our portfolio case, this step involves pulling the portfolio’s details and performance metrics: asset allocation, individual investment returns, risk profile, recent market conditions, fees, any changes in strategy, etc.

You’d also gather the benchmark data for comparison (since underperformance is measured against that).

Remember that any opinions or biases must be kept aside. Just collect information that can help address the question ‘What data and assumptions matter?’.

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Analyze & Test

Once you have the information needed it is then ready to be analyzed.

Why?

In the case of our example, we need to determine what could be causing underperformance. Analysis ensures that your strategy still matches your goals. Carefully reviewing the collated information can uncover areas that can reduce risks or enhance returns.

Various tools can be used to analyze why the portfolio is underperforming. Examples of such tools include:

  1. Morningstar Direct
    Break down returns to see whether underperformance is due to asset allocation, sector exposure, or security selection.
  2. RiskMetrics
    Assess whether the portfolio’s risk profile matches the client’s objectives and benchmark.
  3. Excel
    Check if high fees or trading costs are eroding returns.

Applying the tools
The possible insight we arrive at after applying the analysis tools may be:

‘The portfolio underperformed largely due to intentional but excessive caution (high cash), a concentrated bet on a faltering sector/stock, and routine fees. These were influenced by biases and a lack of course correction.’

We need to be able to test our thinking as we work through our analysis. For instance, as a “test,” you could calculate:

‘If we had only 5% cash like the benchmark, and if we limited stock A to a smaller weight, what would the return have been?’

If that math shows the portfolio would have matched or beaten the benchmark, it quantitatively validates the causes we identified (cash and stock selection).

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Conclude

With our analysis, we can make sense of what has happened. Next, we need to draw conclusions and communicate these thoughts clearly and confidently.

Let’s take our example:
If you find that the portfolio’s weak performance comes from “excessive cash holdings and over-concentration in one sector”, what do you recommend?

You might suggest rebalancing: reducing cash to 5–10%, reinvesting into diversified equities or bonds based on risk profile, and trimming the overweight tech stock from 10% to 5%.

You might also propose a quarterly review process to keep allocations balanced over time.

Each recommendation ties directly to a cause — underinvestment, over-concentration, or process gaps — making it evidence-based.

Context is everything. If the client is highly risk-averse, part of your job is to show why staying entirely in cash can actually increase risk over time, since inflation quietly erodes value. You could show data proving that even a moderate investment strategy could have improved performance.

The goal is to present trade-offs clearly and show that your recommendation is not just logical, but right for them.

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Communicate

Now that you have your conclusions, you need to communicate them with the client. Taking the Bottom-Line Up Front method, lead with the conclusion and follow up with your explanation.

For instance:
‘My recommendation is to rebalance the portfolio by reducing cash from 20% to 5%, reinvesting in a diversified equity fund, and cutting XX stock from 10% to 5%. This should improve performance and better manage risk.’

Then explain the rationale:
‘The portfolio underperformed because it held too much cash and was overweight in a declining sector. Redeploying cash and diversifying will help capture more upside while reducing downside risk. Our analysis suggests this could have added roughly +3% to returns, and with quarterly reviews, we’ll prevent similar gaps in the future.’

Present the conclusion, followed by reasoning and evidence. With this structure, busy stakeholders can grasp the key message in seconds and dive deeper if they want more detail. Ensure that any recommendations made connect back to the analysis.

Coming Next

Now that you’ve covered how to synthesize and communicate your findings effectively, let’s go deeper into one of the most vital skills behind great analysis.

In the next section, you will learn about the power of critical thinking.

Critical Thinking

Critical Thinking

Critical thinking is the skill of performing quality control on your analysis as you work. It means challenging your assumptions, testing your reasoning, and making sure your conclusions hold up.

This isn’t a one-off step; it’s something you do throughout analytical thinking. Ask yourself: ‘Is that really true? How do I know? What might I be missing?’

As humans, even experts, we are all prone to bias and flawed reasoning.

One way we can keep our biases in check is to build habits.

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Practical Habits

A single biased assumption can lead to a bad loan, a missed risk, or a lost client. To avoid that, we need habits that keep our thinking sharp.

Let’s focus on four practical habits you should keep in mind.

To proceed, select each habit to learn more.

What am I taking for granted?
 

Habit 1: Check Your Assumptions

Assumptions are beliefs we treat as facts, often without proof. Unchecked, they can derail analysis. Critical thinkers surface and test them.

When defining a problem or gathering data, ask:
‘What assumptions am I making?’

List assumptions, then validate or revise them. When you catch yourself thinking ‘X is definitely true’ or ‘We must do Y,’ stop and question it. For instance, a credit officer might assume ‘property prices never fall here.’ That belief proved false in 2008. A critical thinker would ask, ‘What data supports that? What if it isn’t true?’

Each time you pause to test an assumption, you reduce blind spots. Ask: ‘What must be true for this to hold? What are we assuming that, if wrong, changes our conclusion?’
What would prove me wrong?
 

Habit 2: Seek Counter-Evidence

We all have confirmation bias, the tendency to favor data that agrees with us. Once we form a view, such as ‘tech is the issue’ or ‘this client is conservative,’ we filter information accordingly.

To think critically, deliberately look for counter-evidence. Ask yourself ‘How could I be wrong? What would show another explanation?’

In practice:

Search for data that contradicts your theory.

Invite an opposing view:

‘Here’s my conclusion. What’s the counterargument?’

Test alternatives:

‘Maybe fees caused it. What happens if I remove that effect?’

The goal isn’t to make sure you aren’t cherry-picking. If your analysis holds after the challenge, great. If not, adjust. In banking, this habit protects against bad loans and overconfidence. It takes humility, but it always strengthens your judgment.
What’s likely vs. what’s certain?
 

Habit 3: Think in Probabilities, Not Certainties

Bankers live with uncertainty, since markets, clients, and economies shift constantly. Critical thinkers accept uncertainty and avoid false certainty.

Thinking in probabilities means saying, ‘There’s a high chance X will happen, but Y could too.’ Instead of ‘This will work,’ say, ‘There’s a 75 percent probability it will work, though a 25 percent chance that market conditions could change.’

By quantifying likelihoods, you set realistic expectations and prepare contingencies. For example, in credit decisions, you might say ‘This borrower has a 95 percent likelihood of repayment, with a 5 percent default risk.’ That informs pricing and reserves.

Regulators value this kind of reasoning because it reflects real-world risk. Using words like ‘likely’, ‘unlikely’, or ‘50/50’ changes how you think. You become more flexible, less surprised, and better prepared.

Probability-thinking builds intellectual humility. You recognize you could be wrong and plan accordingly.
What’s skewing my thinking?
 

Habit 4: Identify the Biases

In Habit 2, we mentioned confirmation bias, the tendency to trust data that fits your view and dismiss what doesn’t. That is only one of many.

Anchoring bias

The anchoring bias makes you cling to an initial number or idea. For example, an analyst might see a stock once priced at $120 and call $90 cheap, ignoring changed conditions.

Availability bias

The availability bias means overvaluing what’s easy to recall. After hearing about fintech collapses, a banker might overestimate the risk of all tech ventures.

Halo effect

The Halo effect is letting one good trait overshadow everything else, such as assuming a company is strong simply because its CEO is impressive.

The best defense is awareness. Use Habits 1 and 2 together to catch these biases early. Keep asking ‘What assumptions am I making? Have I considered the opposite? What’s influencing my view right now?’.

You can also assign a devil’s advocate in team discussions, someone who challenges any assumptions and keeps thinking sharp.

Check Your Assumptions

Let’s use the four habits to reexamine our findings from the scenario.

In our portfolio case, we might assume ‘the client is fine with the strategy’ or ‘tech will rebound soon.’ But these are only assumptions.

Remember to pause and test your assumptions. This reduces blind spots.

During our initial conclusions, it was assumed the client’s cash preference was fixed. Checking that, we might learn it wasn’t deliberate and that they would welcome advice to reinvest. That changes the recommendation.

What am I taking for granted?
What would prove me wrong?

Seek Counter-Evidence

Looking through our conclusions again, we believed that ‘too much cash caused underperformance,’. To check this, we should test whether similar portfolios without cash did better. Or, if you think ‘Stock XX was a bad investment,’ check if others without it still underperformed.

Challenging your analysis is important, it tests if your findings are on the right track and if not, then you are aware something needs to be adjusted.

Think in Probabilities, Not Certainties

Speaking with the client on their portfolio, we should provide only probabilities and avoid giving any false certainties.

Even if we fix the portfolio issues, it would be best to say, ‘We expect to improve performance. If markets behave as usual, there’s a high probability we’ll close the gap.’

What’s likely vs. what’s certain?
What’s skewing my thinking?

Identify the Biases

Moving on to identifying biases, perhaps we soon realize the client had anchored on last quarter’s ‘safe’ cash-heavy strategy, and we can question whether that same logic still holds in today’s rising market.

By surfacing the anchoring bias early, we help them avoid repeating the mistake and instead recommend a data-backed rebalance.

Coming Next

Identify which of these four habits would most improve your decision-making. Practice it deliberately. Over time, these habits help you spot flaws before they turn into real-world errors. That is what separates good bankers from great ones.

You’ve explored the practical habits that can develop and hone your ability to think critically. In the next topic, you’ll explore the importance of data categorization.

Categorization

Categorization

One of the most important skills in analysis is the ability to organize data in a meaningful way. The key to this is categorization. One popular and effective way to do this is the MECE framework.

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Mutually Exclusive, Collectively Exhaustive (MECE) Framework

MECE was developed by Barbara Minto, and it stands for Mutually Exclusive, Collectively Exhaustive. It’s a fancy way of saying you break a complex problem into distinct categories that don’t overlap and together cover all possibilities.

Using MECE ensures you examine all relevant angles without duplication. An everyday example of this might be when planning a family holiday, you can categorize costs as: transport, accommodation, food, and activities.

These categories don’t overlap (mutually exclusive) and cover all main expenses (collectively exhaustive). You won’t double-count meals or forget transport costs. The MECE approach helps make sure every expense is accounted for without confusion.

Mutually Exclusive Collectively Exhaustive

Applying MECE

Using our earlier example, let’s structure potential reasons for underperformance and think of broad categories under which any specific cause would fall.

To proceed, select the arrow to learn more.

 

Categories

Examples of categories that specific causes would fall under include:

External Market Factors: things outside the portfolio’s control (e.g., overall market or sector performance).

Portfolio Composition Factors: choices we made in building the portfolio (asset allocation, stock selection).

Operational/Cost Factors: fees, taxes, transaction timing.
 

Are These Categories MECE?

Are these categories mutually exclusive?

Yes, each cause likely belongs to one bucket.

Collectively exhaustive?

Let’s check: any reason for underperformance should either be that the market did poorly where we invested, or we invested poorly relative to the market, or costs ate returns. That seems to cover it.
 

Analyze the Facts

Now, under each category, we can analyze the facts:

External Market Factors:
The benchmark delivered a positive return of approximately 6%, indicating that overall market performance was not negative. However, the technology sector experienced notable challenges. If our portfolio had been overweight in technology, this external factor would have adversely impacted on the results.

Portfolio Composition Factors:
There are two issues:

(1) Asset allocation:
A cash position of approximately 20% was maintained, which is relatively elevated. Given that equity markets appreciated during the period, this higher cash allocation—and correspondingly lower equity exposure—likely caused a negative impact on overall portfolio performance.

(2) Stock selection:
The portfolio was heavily allocated to the technology sector, with a concentrated position in stock XYZ, which fell 15%.

Performance suffered because of the benchmark’s lower allocation to technology and the portfolio was overly concentrated in one sector.
 

Analyze the Facts - Continued

Operational/Cost Factors: Fees are 1.5% annually, which over 3 quarters is about 1.125%.

The benchmark presumably has no fee. That already accounts for maybe ~1.1% of the underperformance gap.

Taxes are not an issue (tax-exempt account), so we can ignore that. Timing-wise, no trades were made, so no obvious timing errors, though one could argue not rebalancing (i.e. not selling some tech earlier) was a lapse.
 

Analysis Complete

Now we have a structured analysis: the underperformance likely came from a combination of factors.

A strategic allocation gap (too much cash), specific investment choices (tech sector overweight and one bad stock), and, to a lesser extent, the fee drag.

Notice how using categories ensured we didn’t overlook, say, the fee impact while focusing on stocks. MECE helped us be thorough and logical.
 
 

Coming Next

Now that you are familiar with categorizing the data you collect, as well as the potential causes for the problem, you are ready to move onto the next tool that analytical thinking utilizes: Causal Analysis.

Causal Analysis

Causal Analysis

Causal analysis is the main form of analytical thinking used in business. There are two popular techniques that both yield excellent results: The 5 Whys and Root Cause Analysis. Let’s look at both.

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The 5 Whys

The 5 Whys was developed by Sakichi Toyoda, founder of Toyota Industries.

Its goal is to uncover the root cause of a problem by repeatedly asking ‘why’ until you reach the underlying issue.

It’s simple enough to execute. You take a problem and ask ‘Why?’ repeatedly (around five times) to dig beyond symptoms until you identify the fundamental cause.

To proceed, select each step to learn more.

Asking the 5 Whys: excess cash allocation

Now, let’s use the 5 Whys to analyze and discover the core of the problem of the excess cash allocation:
  1. Why was the portfolio holding 20% cash (which underperformed stocks)?
    Because the manager or client decided to stay in cash rather than invest more in the market.
  2. Why did they decide to stay with 20% cash?
    Because they were uncertain about the markets and feared a downturn (let’s say this was the reasoning).
  3. Why were they so fearful of a downturn that they maintained such a high cash position?
    Perhaps because the client had experienced the 2020 market crash and had become very risk-averse (an emotional bias from past experience).
  4. Why wasn’t the allocation adjusted when data showed the market recovering?
    Maybe because nobody revisited the allocation or the team had an anchoring bias – they anchored on the idea that “20% cash is prudent” and failed to update that view as conditions changed.
  5. Why did no one revisit this assumption?
    Perhaps it is a communication issue – the team was on auto-pilot, or there was no trigger or process to review allocation quarterly.

Plausible Root Cause: excess cash allocation

We can stop there; we’ve uncovered a plausible root cause: a combination of client risk aversion and the team’s bias/oversight led to an overly cautious stance (high cash) that wasn’t corrected.

Notice that the first ‘why’ gave a surface answer (they held cash because they were cautious).

By the fifth why, we see deeper issues: psychological biases and process gaps.

The 5 Whys method helps avoid just treating the symptoms (‘too much cash’) by understanding the root cause.

Asking the 5 Whys: stock selection issue

Now, let’s use the 5 Whys to analyze and discover the core of the problem of the stock selection issue:
  1. Why did we have 25% in tech (with one heavy stock)?
    Because we believed tech would keep outperforming.
  2. Why did we believe that?
    Because last year tech had great returns (anchoring on recent success, or perhaps confirmation bias).
  3. Why did we not reduce exposure when tech started falling?
    Because we trusted it was a temporary dip and didn’t want to deviate from the strategy (could be inertia or confirmation bias – seeing what we wanted to see).
  4. Why did we have so much faith in that one stock XYZ?
    Perhaps the client was emotionally attached to it (say it was their former employer’s stock), and we assumed it would rebound.
  5. Why wasn’t there a risk control to limit one stock to 10%?
    Possibly, there was no internal rule, or the manager chose to bend it for this client’s wishes.

Plausible Root Cause: stock selection issue

This root-cause probing shows the influence of cognitive biases (anchoring, confirmation bias) and lack of risk discipline.

It teaches an important lesson: often the roots of underperformance aren’t just market events, but our own thinking errors or process failures. That’s why critical thinking is so vital as the ‘quality control’ of our analysis.

Root Cause Analysis


Sometimes the 5 Whys are not enough. This is where Root Cause Analysis (RCA) techniques can be effective. RCA is a structured way to trace a problem back to its true underlying cause rather than its symptoms. While the 5 Whys is itself a form of RCA, there are other, deeper methods, such as the Fishbone Diagram.

The Fishbone Diagram (Ishikawa) can be useful for complex issues with many contributing factors. It looks like a fish skeleton, with the problem serving as the head and the causes branching out like bones. It was created by Kaoru Ishikawa.

You’d visually map out categories of causes (people, product, process, market, etc.) and brainstorm possible causes under each. If the problem were more complex, you might use this tool to explore all possible angles.

The guiding principle of any RCA method is to identify the system-level cause.

Root Cause Analysis: Portfolio Scenario

In our portfolio case, if we stopped at ‘the portfolio underperformed because it had too much cash and a bad stock pick,’ we wouldn’t have identified the root cause.

The deeper causes we could find might look like this.

Select each part of the fish to reveal the causes.

Cause 1: People

The client’s overly cautious mindset and fear of loss, shaped by past market volatility, led to excessive cash holdings.

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Cause 2: Process

There was no structured quarterly review process to challenge allocations or trigger rebalancing when performance slipped.

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Cause 3: Equipment

The portfolio monitoring tools didn’t flag deviations from the benchmark early enough to prompt timely action.

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Cause 4: Materials

Outdated research and market data reinforced conservative assumptions and delayed corrective moves.

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Results

Together, these reveal a system-level issue: a mix of behavior, process, and feedback gaps that can be addressed through client education and stronger review policies in the future.

Root cause work often identifies small levers that shift big outcomes.

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Coming Next

Now that you’re familiar with the power of causal analysis, you should realize how it can highlight issues and be prepared to take action. The next topic will focus on turning collected data, your analysis, into effective action.

Collection and Synthesis

Collection and Synthesis

We’ve learned how to critically check our thinking and how to analyze systematically. Now we arrive at the culmination: turning analysis into effective action.

In banking, the end goal is always to make a decision, implement a solution, or communicate a recommendation that leads to a result. This is true whether it’s approving a loan, reallocating a portfolio, or advising a client.

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Synthesis

Moving from analysis to action is all about synthesis. This means distilling your complex analysis into a straightforward set of findings and a plan of action covering what should be done about them.

Ask yourself this question: ‘Given all this analysis, what must we do next? What is the one thing the client or decision-maker needs to know?’ Synthesis is the art of boiling down many details into a clear narrative or takeaway.

Let’s take a look at how we might synthesize our client portfolio.

To proceed, select the forward arrow to continue.

 

Our Portfolio Example

In our portfolio case, the synthesis might be:

‘The core issue is the portfolio’s asset mix was too conservative and not reviewed in time. To fix this, we will rebalance to the appropriate risk level and put a review process in place.’

That one sentence captures the essence, leaving out the minutiae. If you have multiple findings, you prioritize them (e.g., major cause vs. minor cause) and possibly combine them if they are related.
 

Phrasing your Conclusions

A good synthesis is often phrased in terms of a solution:

‘We found X, therefore we should do Y.’

Phrase your conclusions as actionable recommendations. Instead of just ‘The data shows our customer satisfaction is low,’ add ‘…thus we should implement A, B, C to improve it.’
 

Making
Recommend​ations

In banking, a recommendation could be ‘hedge this exposure,’ ‘decline this deal until conditions improve,’ or ‘offer the client an alternative product more suited to their needs,’ etc., depending on what your analysis uncovered.

Always tie it back to the goal defined at the start.
 

Analysis Paralysis

Analysis paralysis refers to a situation where decision-making is delayed or completely stalled because of overanalyzing or overthinking a problem.

We need to know when to stop analyzing.

If there is too much data, the number of variables can lead to information overload and decision fatigue, making it harder to prioritize what matters. This increases the risk of overlooking critical details and making errors in judgment.

The key here is to master the art of satisficing. This means choosing an option that is good enough when you have enough information, rather than exhausting every angle until you make the ‘perfect choice’.
 
 

Conclusions

As you return to your day-to-day tasks at Citi, we encourage you to actively apply these concepts.

Start with the next decision you face and try to walk through the four steps. With practice, this will become a habit, and you’ll notice decisions that feel more thorough and made with greater confidence.

Analytical thinking is a skill to be honed continuously. Get feedback after you present a recommendation. Ask your manager or peer, ‘Was my reasoning clear? Did I miss anything?’.

Over time, you’ll develop a reputation as someone whose decisions are rock-solid and well thought out, even under pressure. That is a hallmark of leadership in banking.

Refer to the 4-Step Process


Consider having a checklist at your work. Before sending off that analysis or making that presentation, mentally tick through the 4-step process.

  1. Did I define the real problem here?
  2. Did I get the key data (and not just rely on assumptions)?
  3. Have I analyzed critically and logically and tested my hypothesis (and challenged biases)?
  4. Am I prepared to communicate the conclusion, with a clear recommendation and key points?

If you have all four, you can be confident you’re delivering analytical, critical, and actionable thinking.

Coming Next

You’ve learned about analytical thinking and how to use it to identify the potential causes of a problem and recommend practical solutions. Now, let’s review the key takeaways.

Key Takeaways

Key Takeaways

We’ll conclude this lesson by reviewing the key learning points.

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Recap of What You Learned

In this lesson you learned:

  • The key components of analytical thinking are all actionable concepts that can become habits through practice.
  • How the frameworks can be used to dive into where and why issues can occur and outline potential causes.
  • Evidence-based and well thought reasoning defines problems clearly, provides deeper insight into causes and a structured approach to solutions.
  • Gathering, interpreting and evaluating data, while lacking bias and keeping a clear mind, will improve your decision-making ability.
  • Actively applying the concepts and frameworks explored will hone your ability to break down complex problems and identify solutions.

Coming Next

Now it’s time to check your understanding of the content by completing a short assessment. Good luck!

Claire has identified a potential problem for an underperforming investment portfolio. She reviewed the portfolio’s details, performance metrics, benchmark data, conducted her analysis and plans to send her evaluation to senior management.

Which of the following questions is NOT an aspect of the 4-step model of analytical thinking that Claire can apply to test her thinking in this scenario?

Select the best response from the five options and then select Submit.

Please only use the tab and shift tab keys to access each option and the Submit button with the keyboard. Then only use the Enter or Space key to select an option or the Submit button with the keyboard. The up and down arrow keys are not fully supported. If the screen reader suggests that you use the arrow keys to change an option, please ignore this. Continue using the tab and shift tab keys and then Enter or Space keys to change an option. If you stop hearing the screen reader use the tab key to reset the focus.

Submit

Jason conducts an analysis to uncover the root cause of an excess cash allocation in an underperforming portfolio. He uses the 5 Whys method:

1. Why was the portfolio holding 20% cash?
The portfolio management team decided to stay in cash rather than invest more in the market.

2. Why did they decide to stay with 20% cash level?
There was uncertainty about the markets and fear of a downturn.

3. Why was there such uncertainty and fear?
The client experienced the 2020 market crash and was wary of a reoccurrence, despite data reflecting market recovery.

4. Why wasn’t the cash allocation adjusted given the market data?
The portfolio management team had an anchoring bias–they anchored on the assumption that ‘20% cash is prudent’ and failed to update that view as conditions changed.

5. Why did no one revisit this assumption?
There is no process or control to trigger review of cash allocation levels on a recurring basis.

Can you discover the possible root cause from this analysis?

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Sydney is analyzing her client’s portfolio performance and decides to apply the MECE (Mutually Exclusive, Collectively Exhaustive) framework to organize potential reasons for underperformance. She creates three categories:

  • External Market Factors: Issues outside the portfolio’s control (e.g., overall market or sector performance).
  • Portfolio Composition Factors: Decisions made in building the portfolio (e.g., asset allocation, stock selection).
  • Operational/Cost Factors: Fees, taxes, transaction timing.
Do these categories satisfy the MECE principle?

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Which statement best reflects the importance of gathering, interpreting, and evaluating data with an open mind and awareness of bias?

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Which of the following is a key component of analytical thinking?

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Introduction

The Core Elements of Analytical Thinking

Critical Thinking

Categorization

Causal Analysis

Collection and Synthesis

Key Takeaways

Assessment

Home
Introduction
The Core Elements of Analytical Thinking
Critical Thinking
Categorization
Causal Analysis
Collection and Synthesis
Key Takeaways
Assessment

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