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Financial statements are incorrect for several reasons:
Our "Financial Health Check-Up" finds anomalies. Our investigation goes deep; we look at 100% of your transactions.
Your shareholders, investors, board, and employees rely on your numbers to make wise decisions — efficient allocation of capital. Errors create uncertainty and damage your company's reputation. Our financial risk analytics produced by artificial intelligence identifies anomalies.
MindBridge Ai Auditor is a revolutionary approach to detect anomalies - unintentional and intentional - by automating manual processes and providing a risk-based assessment to help organizations ensure compliance and minimize their exposure to financial loss and liability.
The Ai Auditor bridges the gap between human and artificial intelligence (AI) and enhances professional judgment. Advancements in AI enables a new class of analytics to analyze financial transaction flows to detect mistakes, errors and fraud.
The Ai Auditor generates actionable insights through an interactive, user-friendly visual interface to help organizations minimize risk exposure to financial loss. It supports audit professionals to meet and exceed regulatory and industry standards. Industry leading professionals have proven that the Ai Auditor detects unusual human activities and transactions that incumbent systems cannot. In fact, the Bank of England selected the Ai Auditor in a worldwide competition.
The Ai Auditor uses artificial intelligence, machine learning, and traditional rule-based analytics. Initially developed for forensic auditors, it is fully compliant with SAS 99, CAS 240, and ISA 240 and more! It is shaping the next generation of audit standards by working closely with academia and professional organizations.
We are making this high-value service available to organizations of all sizes. Depending on your needs, we provide reports: daily, monthly, quarterly, or annually.
Anomalies occur in GLs. Our service finds them. Moreover, shareholders and regulators rely on correct financial information to make critical decisions. You do too! Understanding, early detection and correction of anomalies are critical to having the right data for managing operations, decision-making and protecting your reputation.
It is sad to say that the rate of fraud is increasing. The length of time to detect fraud is also increasing. Rules-based systems and data sampling are losing this battle. Clever people will figure out a way around rules. In fact, the higher the education level of the perpetrators, the higher the loss the organization suffers. To compound this problem, Lexis Nexis estimates that the cost to correct $1.00 of fraud is $2.40.
We have a better approach.
How does the financial risk assessment service work?
Our service gives you information to find problems.
Our reports give outcome measures of your financial information quality, reliability and accuracy. They provide a factual basis for quality improvement initiatives related to your reporting and internal controls. The report will show if your changes have strengthened your system.
| How Fraud is Detected | Percent of Cases |
|---|---|
| Tip | 0.37 |
| Management Review | 0.143 |
| Internal Audit | 0.141 |
| By Accident | 0.072 |
| Account Reconciliation | 0.061 |
| Other | 0.055 |
| Documentation Examination | 0.048 |
| External Audit | 0.04 |
| Law Enforcement Notification | 0.025 |
| Surveillance Monitoring | 0.019 |
| IT Controls | 0.015 |
| Confession | 0.012 |
Our service is confidential and independent. We leverage advanced technology, including artificial intelligence, new advancements in machine learning, and data science for near real-time data analytics, pattern recognition and anomaly detection. We use over 27 control point indicators testing techniques on every GL entry.
Many organizations conduct internal reviews and assessment of financial transactions monthly, quarterly or semi-annually. The volume of financial transactions can be vast and need a team of full-time staff to work on the data. Some organizations use spreadsheets with manual and visual inspection to check, validate, and reconcile the information. These tasks are labour-oriented, time-consuming, tedious and expensive. Internal audit processes only find about 14% of fraud cases.
On the other hand, audit testing approaches use sampling methods and materiality thresholds. A regular audit usually sample tests less than 3% of the GL information. This approach only detects about 4% of the fraud that is eventually found.
In contrast to the costly and time-consuming approaches used for the validation of financial transactions, our service looks at 100% of the GL entries. We do it quickly and pinpoint anomalies. You investigate if the abnormality is accurately reflected, what occurred and take corrective action as needed. It saves time and money whether it is investigating past activities, detecting unacceptable behaviour or preventing possible transgression.
| Algorithms automatically performed | Increased Value with MindBridge Analysis |
|---|---|
| Business Rules | Packaged library of rules that adhere to industry standards |
| Industry Standards | Meets industry standards to detect known financial misstatements |
| Statistical Models | Built-in recommended statistical tests |
| Industry Statistical Models | Analysis includes industry recommended statistical model tests |
| Machine Learning | Built-in proven machine learning tests based on adaptation of proven methodologies |
| Automated Machine Learning | Enhanced analysis with automated machine learning tests to uncover unknown financial misstatements and errors |
Use the same or more powerful tool as your auditor. We support your internal audit group with the powerful MindBridge platform. We recommend you start with a service to see the value. Over time you may wish to incorporate it into your internal audit processes.
Use the most powerful tool to guide your audit to the highest risk areas. We are an authorized agent for MindBridge AI. We would be happy to set up your audit firm with the powerful Ai Auditor. We sell packages from 2 cases to 300+ cases.
Anomalies in financial transactions and GLs tell a lot about a company’s character. Learn more about how our service in mergers and acquisitions work. We pinpoint where you should look in your due diligence process.
Every report is available in downloadable format, whether it’s images from a screen or some form of data tables. For example, our data can be exported to a Microsoft Excel file and attached as a supporting document to your audit report. In fact, we highly recommend taking all the data we provide and showing them to your client, where it won’t cross independence lines, so they see you as the expert, trusted partner you should be along with the evidence to back it up.
You can also produce reports to share with your end clients including income statements, financial trending analysis, financial analysis, and more.
For more information on Ai Auditor or to book a demo.
Customer data is secure. It is always protected, using NIST-approved algorithms (AES 256) and the most secure protocols and implementations available. All network connections are encrypted and all data stores, including primary and backup, are encrypted at all times.
For more information on Ai Auditor or to book a demo.
MindBridge Ai cloud services are hosted on a secure cloud infrastructure. Our primary and backup providers fully ISO 27001 and SSAE 16 compliant. Our software stack is designed for defence in depth, deploying redundant controls in the infrastructure, network, platform, and application to ensure no single point of failure. Data stored is secure.
For more information on Ai Auditor or to book a demo.
This one is critical to understand: Our platform isn’t just for performing year-end audits, rather it plays an important role throughout the year, including planning. Our interim analysis is always available, going back to whatever period is available from the data, to help you see and understand how the business is transitioning at various points in time.
We support planning in different ways, such as looking at the data to identify and prioritize where you should be spending more time. It could be a potential risk in inventories or accounts payable, or really anything that could influence your thinking around how the business is performing. Additionally, we also give you all those control points to show exactly what’s going on in the business and we can help you derive insights from the available data.
We want you to see and drill down into where the risks are at any point in the year, all for the same price as doing a single engagement at the end of the year.
For more information on Ai Auditor or to book a demo.
We provide a drag and drop interface to load your data and integrate with the most common ERP systems used today, things like CCH Engagement, QuickBooks, Thomson Reuters AdvanceFlow, NetSuite, Sage Intacct, and more, to pull the various types of data we need. For something like accounts payable, for example, we use information from the ledger itself, including the payables register at the end of the period so we can see what’s outstanding and things such as the vendor name and the user hierarchy.
We also eliminate the need to spend time or IT resources on data extraction, manipulation, and ingestion – we take care of all the data heavy lifting so you can focus on the analysis and results.
For more information on Ai Auditor or to book a demo.
The honest answer is that it depends on what you want to accomplish. If you’re just using a working paper solution to gather data to do quick assessments of a trial balance, our platform would absolutely be an addition to what you’re already using. You would use it to go even deeper into the analysis of the data and bring all our reports back into your working papers to have a much higher level of confidence. On the other hand, if you’re using a data analytics tool, especially a visual tool that doesn’t have machine learning built into it, Ai Auditor could potentially be a more effective and easier to use replacement.
We never say it’s one way or the other because every firm we work with has a different view of how technology supports their people and engagements and how they look at things from a line of business perspective, for example M&A, or all the way through to assurance audit and taxation.
At the end of the day, it really depends on the use case but one thing is certain, Ai Auditor is a tool used to help people be more effective at understanding data and gathering evidence, in the capacity that best suits their needs.
Our goal is to minimize training to make the platform easy to use – a different philosophy from some of the old audit tools you may have used in the past. We designed Ai Auditor to be as user friendly as possible to help you get to maximum value as quick as possible, which means you need no programming or scripting skills to get things done. It’s all drag and drop actions, mapping your data, running the analysis, and viewing results in as easy a manner as possible.
Of course, we do recommend and include training on using the platform itself. Typically, that’s a kickoff with our customer success team to show you around the platform and help you load in that first data set. We give you a few days to play around with the data and reports, then set up a more focused discussion to help you get the most out of the results, such as understanding what control points do and what the machine learning algorithms are hunting for.
The rapid surge in the price of Bitcoin has brought discussion on cryptocurrencies and distributed ledger technologies (DLT) into the mainstream. But why all the hype? Before diving into such topics, it is important to have a foundation on the technology behind cryptocurrencies: blockchain.
Blockchain is shorthand for a whole suite of a distributed ledger technologies that can be programmed to record and track anything of value, from financial transactions, medical records or land titles.
This video is an accessible introduction to blockchain technology: how it tracks and stores data, how it fosters trust, and how it facilitates peer-to-peer transactions without the involving middlemen like bankers or lawyers.
Blockchain’s potential for underpinning online interaction is limitless. But, much like the rise of the internet, this new technology brings with it complex questions around governance, international law, security, and economics.
The blockchain is a public ledger where transactions are recorded and confirmed anonymously. It’s a record of events that are shared among many parties. The blockchain is actually managed by distributed nodes. These nodes all have a copy of the entire blockchain. Nodes will forever come and go, synchronizing their own copies of the chain with those of other users. By distributing copies and access, the chain can’t simply “go down,” or disappear. It’s a decentralized system that is both sturdy and secure. More importantly, once information is entered, it cannot be altered. Here is blockchain explained in fewer than 100 words
You (a "node") have a file of transactions on your computer (a "ledger"). Two government accountants (let's call them "miners") have the same file on theirs (so it’s "distributed"). As you make a transaction, your computer sends an e-mail to each accountant to inform them.
Each accountant rushes to be the first to check whether you can afford it (and be paid their salary "Bitcoins"). The first to check and validate hits “REPLY ALL”, attaching their logic for verifying the transaction ("Proof of Work"). If the other accountant agrees, everyone updates their file.
This concept is enabled by "Blockchain" technology.
Yes - but as a concept, not much more. Complexities come in the implementation and the journey to realize value from such implementations. The above example will, of course, be overly simplistic for some — but may be a starting point for others.
In a traditional environment, trusted third parties act as intermediaries for financial transactions. If you have ever sent money overseas, it will pass through an intermediary (usually a bank).
It will usually not be instantaneous (taking up to 3 days) and the intermediary will take a commission for doing this either in the form of exchange rate conversion or other charges.
The original blockchain is open-source technology which offers an alternative to the traditional intermediary for transfers of the crypto-currency Bitcoin. The intermediary is replaced by the collective verification of the ecosystem offering a huge degree of traceability, security, and speed.
In the example above (a "public blockchain"), there are multiple versions of you as “nodes” on a network acting as executors of transactions and miners simultaneously. Transactions are collected into blocks before being added to the blockchain. Miners receive a Bitcoin reward based upon the computational time it takes to work out:
As more transactions are executed, more Bitcoins flow into the virtual money supply. The "reward" miners get will reduce every 4 years until Bitcoin production will eventually cease (although estimates say this won't be until 2140!). Of course, although the original blockchain was intended to manage Bitcoin, other virtual currencies, such as Ether, can be used.
There are three reasons why you need to know about Blockchain:
Source: Blockchain explained... in under 100 words — Richard Bradley, Director, Deloitte
The number of accounting scandals is amazing. Most result in re-stating the accounts and some in major scandals. The issue of regulatory oversight is frequently in the press. However, even with enforcement increasing and getting penalties higher, a new accounting issue is never far behind. It continues to get worse. Just look at the impact on British Telecom’s stock because of the irregularities found in accounting in their Italian division (read about it here).
At MindBridge Ai focuses on solving one of the massive issues underlying these scandals. We are building an expert system to help both internal and external auditors. This provides us with higher levels of assurance about the financial statements they are sworn to certify.
And the baseline issue we are dealing with is the amount of financial data they need to consume. There has always been too much of it.
With Big Data, auditors are asked to “go fishing in a murky lake” and find accounting anomalies and irregularities for further review. Accountants and regulators agree to use statistical account sampling. Yet, the quality of the sample can only be measured after it has been sampled and analyzed. With the time pressure on providing audited results, if an irregularity or anomaly is found, the process has to start again. Moreover, this leads to costly re-statements which impact shareholder value and confidence in the audit firms.
To solve this, Mindbridge Ai leverages machine learning AI to provide auditors with a more highly justifiable sample before the audit begins. Our Ai Auditor service ingests and analyzes the entire leger. We pinpoint areas they should be investigating early on with a higher degree of justification against the standard, and currently accepted sampling techniques. This gives auditors a higher assurance rate. It helps them improve upon the current approaches that only meet the standard, and not move past it. This moves towards the answers we, the press and firms like British Telecom need.
enhancing professional judgment with faster results and more profound insights
MindBridge is bridging the gap between human and artificial intelligence by improving expert understanding across multiple industries. Recent advancements in artificial intelligence have enabled a new class of converged analytics to analyze financial transaction flows and better detect anomalies - unintentional mistakes/errors and intentional. Using algorithms based upon smart data science, MindBridge generates actionable insights through an interactive, user-friendly visual interface to help organizations minimize risk exposure to a financial loss while supporting audit professionals to meet and exceed regulatory and industry standards. Using the MindBridge Ai-Auditor is enhancing professional judgment as far less time is spent searching for anomalies and more time determining the underlying causes of the anomalies.
The MindBridge application has been tested by industry leading professionals and is proven to detect anomalous human activities and transactions that incumbent systems cannot.
Website: http://www.mindbridge.ai/intacct
Year Founded: 2015
MindBridge Ai Auditor is a revolutionary approach to analyze financial transactions to detect anomalies - unintentional and intentional - by automating manual processes and providing a risk-based assessment to help organizations ensure compliance and minimize their exposure to financial loss and liability.
The MindBridge AI Auditor is bridging the gap between human and artificial intelligence (AI) by enhancing professional judgment across multiple industries. Recent advancements in artificial intelligence have enabled a new class of converged analytics to analyze financial transaction flows and better detect anomalies - unintentional mistakes/errors and intentional.
Using algorithms based on data science, MindBridge generates actionable insights through an interactive, user-friendly visual interface to help organizations minimize risk exposure to the financial loss while supporting audit professionals to meet and exceed regulatory and industry standards. The MindBridge application has been tested by industry leading professionals and is proven to detect unusual human activities and transactions that incumbent systems cannot.
The tools currently used to analyze financial transactions are not keeping pace with the needs of today’s auditor and investigator professionals. Existing computer-assisted audit tools are often time-consuming to use and do not provide the insights and analytics required to help effectively advise auditors or develop a comprehensive financial analysis. MindBridge offers intelligent audit technology to help increase the speed and accuracy of every audit and investigation.
Current audit and investigative techniques are limited by time and tools, restricting the financial analysis to random sampling and data filtering. What if you could review every financial transaction in the general ledger from every entry across every department?
With MindBridge there is no data set too large, meaning the system is capable of reviewing 100% of the data provided and calculating a risk score for every process or person that interacts with corporate data. This is how you can find more instances of financial misstatements suspicious financial discrepancies.
Intelligent anomaly detection to help you:
Automation is key to maximizing the effectiveness of each financial audit or review your organization performs. By removing manual processes, it is possible to streamline time-consuming activities, which frees up team members to use their expertise more effectively.
MindBridge’s Ai Auditor helps you be more efficient by offering:
An unbiased analysis of each transaction history based on numerous control points and data science algorithms.
For maximum impact, the system is intuitive requiring little to no training meaning it can be used by anyone for the most basic of investigations, audits and all the way up to a forensic examination.
A visual summary of all transaction data and risk scores so that each auditor and investigator can determine where to focus instead of using sampling techniques alone.
Automatically assigns a risk score to all transactions to focus audit, 100% coverage of all transactions.
Intacct Web Services, modern web-browser (Chrome or Firefox are preferable)
Price: contact: kim.allen@allenvisioninc.com
Machine learning is the subfield of computer science.
computers have the ability to learn without being explicitly programmed
It evolved from the study of pattern recognition and computational learning theory in artificial intelligence. Data scientists construct algorithms that learn from the data and adapt over time.
The focus is statistical learning. Machine Learning deals with predictions and not causality. It is effective at developing more complex relationships than humans can process. It performs well in nonlinear and interactions settings.
Examples of Machine Learning Application
Machine learning compares the data set by looking at Row 1 and its columns vs Row 2 and its column data. Then, Row 3, etc. The MindBridge Ai-Auditor ingests 100% of the file. It then analyzes 100% of the file with 100% of the rest of the file. This is hard to explain as this is the background of how the MindBridge Ai-Auditor works, but it takes data from the GL and breaks them down into ‘nodes’ where the machine learning occurs.
We use this approach in all of our financial risk assessment services and examine every transaction.
The example follows a simple use-case: CITY POPULATION. Imagine every dot represents 1 person in a city.
Data Set
Those circled by Green show that it’s within normality. In that, the large part of the population lives near to one another. There isn’t anything odd about where they live. That’s not to say that once you dive in you won’t find anything (there might be cases of crime, theft, etc), but from a high-level, it looks normal.
Then, Orange is starting to show that the distribution of people is starting to scatter. This is odd. Why is that? Why are they living slightly further away from the general population?
Now, those in Red are real outliers. Why are they so far from not only the general population but also from one another? What is the story behind these dots?
This is what you would consider a High-Risk %, and would need further analysis.
Think of MindBridge Ai-Auditor like that when it breaks the file down. Think of machine learning as everything a human cannot do. There is absolutely no way a human would be able to ingest 100% of the file for 100,000 Journal Entries. More so, a person would not be able to properly map out every single entry into a format that makes sense.
At present, the MindBridge Ai-Auditor only 1 GL can be analyzed. It’s ideal to have 12 or more months of data rolled-up into 1 file.
Cohort Analysis (ie., analyzing multiple files, such as 12 files where 1 file is 1 month) is something that’s coming in the future in our sub-ledgers update.
Read Mansbridge's Story.
There is not a 'one size fits all' answer to answer the question: "What is the minimum sample size required to train a deep learning model. The amount of training data you require is dependent on many different aspects of your experiment:
Data scientist work to squeeze maximum advantage from a small training dataset to give the best results.
Traditional analytics rely on rule-based methods for anomaly detection. These decision rules follow simple Boolean logic: if a vendor address matches an employee address and its wire transfer account matches the employee bank account, then it is likely a fictitious vendor.
Machine learning is a useful type of AI that is able to learn without predefined decision rules. Machine learning constructs its own decision tree based on meta-tagged data, e.g., “red flag” or “not,” to decide how “red flag” transactions are related. Machine learning applies the learned logic to new data and is remarkably adept at making the right decision. Machine learning’s ability to learn from a complex array of data and not just a few variables leads to greater accuracy.
Another type of machine learning, called “unsupervised learning,” constructs decision trees without meta-tagged data; it identifies patterns of interest and anomalies using its own decision-making criteria. This allows fraud examiners to find new forms of fraud not previously detected or codified into rules-based methods. Both supervised and unsupervised machine learning systems are self-refining, in that they become more accurate as more data is encountered.
The MindBridge Ai-Auditor machine learning translates every incoming GL into a language the machine understands (the 5 account mapping feature). Because of this translation, the machine is able to understand the monetary flows within a GL and identify unusual transactions. Traditional CAAT tools don’t do that, they simply scan numbers in isolated (for example show me all weekend entries). They don’t need to understand the GL to find such transactions.
Machine learning translates the GLs use in our suite of financial compliance risk services.
Existing practices in financial anomaly detection typically use rules-based systems to find breaches of control. An example of such a rule is a transaction amount limit.
Using rules-based systems is the backbone of today’s auditing methods. However, often this approach fails to find material financial misstatements and fraud. Clever fraudsters understand these rules and get around them. Rules only capture what is explicitly coded into them.
Rules are designed and implemented for each known circumstance that requires control or management. This means rules based systems will not catch unanticipated scenarios. Even with the example of the transaction amount limit, the commonly known way to ‘work around’ simple limit rules is transaction splitting. Often the rules themselves create the opportunity or scenario for the exploit. The data from the Association of Certified Fraud Examiners shows that the higher the education level of the perpetrators, the higher the loss the organization suffers and in most scenarios the perpetrators have at least a University level education.
The most likely means of which a fraudster gets caught today is through a tip-off, roughly 40%, and industry associations tell organizations to add “fraud hot lines” to gather these tips. Conversely, automated controls and IT systems based on rules only catch a small percentage of scenarios — approximately 3%.
The rate of fraud is increasing and the time to detect fraud is also increasing. In almost every measurable way rules based systems which use data sampling are losing this battle. To compound the problem, Lexis Nexis estimates that the cost to correct $1 of fraud is $2.40.
It’s time for a change because clever people will figure out a way around rules.
It is often the case that it is not a single transaction that indicates an anomaly but a group of transactions such as a recurring monthly event. As such, normal outlier detection does not work as it is often a set of transactions that individually may seem to be normal or acceptable practices but collectively do not follow normal or acceptable practices. To address this, outlier detection is built in several layers to consider how rare a given business process may be and the outlier score of transactions based on other transactions in its group, and the ledger.
Anomalies such as financial misstatements, or fraud, are often not detected because they are carefully hidden among other similar transactions. Sophisticated fraudsters hide their work among similar account interactions and values.
This is where outlier systems need to consider many dimensions of the data at once. While human beings are good at seeing an outlier in 2 or 3 dimensions, a machine can do the same work tirelessly, considering 10 or more dimensions at a time. This capability helps machines to spot the outliers which are hiding next to normal activity and within an established process.
Learn more about using our advanced analytics for financial risk assessment.
Below is a comparison of MindBridge versus traditional audit analytics tools.
Criteria | Traditional Audit Analytics Tools | MindBridge | Winner |
Coverage of testing | 100% of known areas of interest | 100% of known and unknown cases | MindBridge |
Proven usage | Trusted, but outdated technology | Breakthrough technology, with growing evidence of its effectiveness | Tie (MindBridge positioned to surpass) |
Knowledge curve (coding) | 100% of known areas of interest | Not required | MindBridge |
Ease of use (user interface) | Cumbersome, requires repetitive, extensive user training | Highly visual, intuitive with little-to-no training | MindBridge |
Template (speed) | Time-consuming to set up rules for each client file | Not required | MindBridge |
Availability | Dependent on ERS resource | Self-service | MindBridge |
Composition & depth of algorithms | Rules are hand-crafted scripts and results are logged. | Packaged industry standard rules enhanced with automated, scientific algorithms, results are logged and exported | MindBridge |
False-positives handling | Rules are created to handle each case | Once flagged, machine learning automatically excludes them from future analysis | MindBridge |
Data ingestion | Manual, labor-intensive | Built-in, automated “smart ingestion” leveraging machine learning | MindBridge |
Adherence to Audit Standards (IAS 240, CAS 240 & SAS 99) | Subjective & selective: reliant on professional skepticism and experience to find potential financial misstatements | Objective & inclusive: AI risk-based analytic tests prescribed by the Center for Audit Quality, plus extended analysis that further improves audit assurance | MindBridge |
Check out how we use the MindBridge Ai-Auditor for financial risk assessment.
This is an excellent approach. Most organizations under 500 employees do not have an internal audit process. It will give you facts to show those who think you are too small that you are exposed to the same risks as big companies.
Today organizations face an unprecedented confluence of risks. Two-thirds of CEOs see more threats to their business than opportunities. To stay competitive, you must pursue two parallel strategies: risk resiliency and risk agility.
Forward-looking companies have both the solid infrastructure and processes to help them weather any storm, as well as the flexibility to move quickly to meet new opportunities.
A comprehensive internal audit function plays a critical role in helping your organization achieve good governance—by providing an independent and objective assessment of your risk management strategies and control frameworks.
Check out our internal audit service and how we can support your internal audit team.
You can learn about how we use advanced financial compliance analytics to conduct a financial risk assessment in audit, internal audit and financial due diligence for merger and acquisition work.
You may find the Top 14 Financial Frauds of All Time to be of interest.
Machine learning is the subfield of computer science.
computers have the ability to learn without being explicitly programmed
There are many machine learning applications.
Machine learning evolved from the study of pattern recognition and computational learning theory in artificial intelligence. Data scientists construct algorithms that learn from the data and adapt over time.
The focus is statistical learning. Machine learning deals with predictions and not causality. It is effective at developing more complex relationships than humans can process. Machine learning performs well in nonlinear and interactions settings.
Examples of machine learning application
Learn about how we use machine learning for financial risk assessments.
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Kim Allen brings clients a big-picture view that balances innovative thinking with the practical mindset that can only be gained by leading organizations. He draws on his 25+ years of accomplishment as a chief executive in the public, private, regulatory, and association sectors and experience across all major business functional areas. As an agile leader, Kim works with business leaders around the world to build and strengthen governance, strategy, organizational alignment, and operational excellence. Also, he works with clients to find and develop exceptional leaders. Kim cares about the people and understands the bottom line.
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