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 risk strategies: resiliency and agility.
Advanced analytics are an essential part of the solid infrastructure and processes that forward-looking companies use to weather any storm and move quickly to meet new opportunities. We provide financial analytics to support your internal audit function. Internal audit plays a critical role in helping your organization to achieve good governance by providing an independent and objective assessment of your risk management strategies and control frameworks.
How much should I budget to spend on an internal audit function?
Companies spend on average $3,900 per million dollars of revenue ranging from $2,500 to $14,000.
We have scaled our services to enable your organization — big or small — to benefit from using our advanced analytics.
Our internal audit service provides you with an independent appraisal function that examines and evaluates the effectiveness of existing systems, procedures and activities within the organization.
Our primary goal is to make sure that senior management and the Board (through its Audit Committee) can provide assurance that:
- operations were transacted with sound business judgment and high ethical conduct
- assets are safeguarded
- financial records are reliable
- internal controls are effective
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We also add value by assisting management and staff in effectively carrying out their responsibilities and achieving their goals. In an expanding risk landscape, internal audit has emerged as a critical lever for change. We are driving internal audit innovation using leading edge approaches and the latest financial analytics, governance analytics and people analytics. The result helps you to build the confidence to act decisively and move faster.
Our solutions match the needs of your business. We look deeper by considering areas like your organization’s culture and behaviours to help you improve and embed control. Our work is impactful. We work with you to set up the right risk level to add the greatest value to your organization.
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Kingston Smith UK Chartered Accountancy adds MindBridge AI Auditor technology to its data analytics suite
Thomson Reuters Tax & Accounting Reaches Agreement with MindBridge Analytics Inc. to Deliver Data Analytics Capabilities as Part of Audit Suite
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Seven Reasons to Hire Allenvision
Many consultants will tell you what to do — after we jointly determine what need to be done — we work with you to show you how to do it. While we are happy to undertake the complete implementation of a project, we prefer to coach and teach your team how to do it. We believe that success growth is mutually beneficial for us.
From filling a short-term executive gap to the implementation of a major program of organizational structural change — hire Allenvision. We provide valuable expertise and insights to help you achieve your goals and execute a strategy.
When is the right time to hire Allenvision?
What on-going steps should you take to ensure you get the best out of a client-consultant relationship?
- External validation: Allenvision has a broad overview, understanding, and external perspective. We use an evidence-based approach in all of our work. A second opinion can provide reassurance before making a critical business decision.
- More time and cost-effective: We focus on a project and see it through on deadline, without distractions and day-to-day pressures. This often makes bringing us in much more time and cost efficient than running a project in-house.
- Specific knowledge, skills, and experience: Allenvision gives you the opportunity to bring in niche skills, without the commitment of employing someone.
- Ability to challenge: Our objective position means Allenvision can bring a fresh perspective. We are not afraid to challenge, and our unique position means we can do so without the fear of reprisals that your employees might have.
- Impartial advice: Hiring Allenvision can offer you a way to reach or justify a desired conclusion and avoid internal conflict. This can be particularly valuable in stressful situations such as job cuts and significant operational or strategic changes.
- Knowledge of best practice: Allenvision works with multiple clients and often serving various clients facing similar problems across different sectors.
- Access to information and resources: Allenvision specializes in dealing with matters related to people, money, and governance. We can bring in data and systems that may not be financially viable for your company.
Overall, Allenvision brings a wealth of strengths to your business and can deliver a full range of services. So, if you are seeking a solution to a particular business problem, developing your employees, creating succession plans, undergoing organizational change or can see new market opportunities but lack the resources to follow them up, Allenvision may be the answer you need.
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MindBridge is bridging the gap between human and artificial intelligence 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 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 current incumbent systems cannot.
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.
MindBridge AI Auditor - Enhancing Profesional Judgement
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.
Financial Transactions Analysis, Evolved
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.
Comprehensive analysis for accurate audits
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:
- Uncover more misstatements & potential fraud by reviewing 100% of corporate data
- Know where to look due to intelligent risk-scores
- Smarter control points that combine best practices and data science
- An intelligent system leveraging machine learning that adapts to your needs
Take financial audit efficiency to the next level
Automation is key to maximizing the efficiency 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:
- Smart data ingestion of your financial records saves you time importing and shaping data files
- Automated risk-scoring is based on a blend of control points and anomaly detection algorithms to help guide you to entries that appear suspicious
- Automated insights recommend similar items to look at and which steps to take next
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 investigation.
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.
Better Meet Audit Standards
- Leveraging Artificial Intelligence to meet and exceed standards including:
- SAS 99 Fraud in a Financial Statement Audit
- IAS 240 / CAS 240 The Auditor’s Responsibilities Relating to Fraud in an Audit of Financial Statements
- Know where to look due to intelligent risk-scores
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: email@example.com
One of the perhaps the most obvious and yet still most consistently recurring municipal corruption related problems for local governments is conflicts of interest, possibly due to the many and varied circumstances in which such conflicts may arise. The prevalence of this type of corruption is demonstrated by the amount of legal authority and case-law available on the topic.
- the offering, giving, receiving, or soliciting of any item of value to influence the actions of an official or other person in charge of a public or legal duty
- taking money to give people preferential treatment. identified areas of concern include bribes from developers in permitting process, payback for zoning decisions, equipment contracts, or service contracts, bribery of building inspectors to obtain permits, bribery of elected officials for development variances and approvals, bribery of planning staff to obtain recommendations for development approvals
- may take the form of inappropriate gifts/sponsorship such as hockey tickets and other gifts for politicians and/or staff
- cronyism may include awarding contracts to people affiliated with the municipality or corrupt official, patronage appointments based on connections rather than qualifications, awarding contracts at inflated prices
- nepotism may include such issues as favoring family members in municipal hirings, zoning regulation changes based on friendships among colleagues rather than disinterested analysis
- misappropriation of money or resources under a local government official or employee’s control
- making false claims for benefits in order to abuse systems such as social security
- occurs when a public official forces someone to give them benefits in exchange for acting/ not acting in a particular way, or when an external actor does the same to a public official
Conflicts of Interest
- a personal interest in a matter that goes beyond the interests of other members of the community, and might reasonably be expected to influence the elected official’s performance of his or her duties e.g., close links between developers and city staff, campaign contributions from developers, conflict in contract awards, personal interest in administrative decisions
Breach of Duty
- local government officials and employees ignoring applicable municipal legislation, e.g. sale of municipal assets for less than market value
Misuse of Authority
- lack of transparency/democratic concerns such as inappropriate use of in-camera meetings, non-public altering of official records, dishonesty concerning legislative options
- fraudulent use of expense accounts such as reimbursing inappropriate expenses, double expensing – influence on independent third party bodies, such as boards, which are intended to be at arm’s length
- including organized crime, internal theft/fraud, petty theft/fraud
source: Municipal “Best Practices”: Preventing Fraud, Bribery and Corruption – Elizabeth Anderson
It’s important to note that an unusual transaction depends on the context. An unusual transaction for one company might be completely normal for another company. The internal audit team has a better sense of what’s normal in an organization.
In our fraud case, study the CFO reversed Cost of Goods sold. It was flagged as highly unusual.
Machine learning is often used to spot “red flag” patterns in structured data (data with a predictable structure, like spreadsheets, databases, and financial data formats). Examples an unusual transaction include identifying suspicious insurance claims, unusual banking transactions, and credit card activity.
Machine learning is also useful in network relationship analysis. In this application, machine learning explores the connections between people and entities. Often complex, relationship networks are quickly quantified with an unsupervised learning approach called “clustering” allowing the examiner to efficiently find key relationships and the web of communications and influence. The source of such data is often corporate email, but may also include phone records and social media.
We believe in the analysis of 100% of transactions to detect anomalies and not in the sampling of data. We also believe that sensible business rules based controls can help, to comply with existing audit recommendations. Vitally important is that these methods are combined with advanced analytics and data science.
You don’t want to choose one approach or the other, but instead a blended approach.
Use of Machine Learning
It much improves the effectiveness of anti-fraud controls by identifying outliers and anomalies by combining rule-based techniques with robust algorithms. This enhances accuracy based on what they see. Machine learning uses modeling and makes data-driven predictions about a given situation. Machine learning is one way for the system to feed what it learns back into the anomaly detection engine. The more exposure to data they have the smarter they become. This is important because it alleviates the manual rules maintenance and decision-making that has proven slow and ineffective in the previous generation of financial anomaly detection.
By doing this, the investigator have access to not only a data scientist in a box but a virtual forensic auditor that brings potential misstatements to your attention. At the same time the auditor or internal auditor can select from a number of well accepted fraud scenarios (control points). They have the financial statement reviewed based on his/her risk-assessment and comply with company policy and audit standards.
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.
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Data for machine learning
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:
- How different are the classes that you're trying to separate? e.g. if you were just trying to classify black versus white images then you'd need very few training examples! But if you're trying to solve ImageNet then you need training data on the order of 1000 examples per class.
- How aggressively can you augment the training data?
- Can you use pre-trained weights to initialise the lower layers of your net? (e.g. using weights trained from ImageNet)
- Do you plan to use batch normalization? It can help reduce the amount of data required.
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.
Tip-off most likely way to catch a fraudster today
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.
Yes. CAS 240 is the Canadian Audit Standard that deals with the risk of management override of control. This standard needs to be met in all financial audits regardless if the auditor’s risk assessment gives concern for such an override or not. This risk is unpredictable and requires special audit considerations.
The main approach suggested in CAS 240 to address this risk is to test journal entries for material misstatements due to fraud or error. CPA Canada acknowledges that practitioners struggle with this goal. The wide-spread use of sampling techniques to select journal entries for testing gets increasingly criticized.
CAS 240 Compliance — Five step approach to guide auditors
To assist practitioners we propose a five step approach to guide auditors through the process of identifying and testing journal entries:
- Understand the information system and business processes relevant to financial reporting.
- Make inquiries of people about inappropriate or unusual activity on the processing of journal entries and other adjustments.
- Select the journal entries and other adjustments with characteristics of potentially inappropriate journal entries and other adjustments.
- Test the appropriateness of journal entries and other adjustments.
We describe each of these steps in detailed documentation and show how our approach meets and exceeds the CAS 240 requirements. It is a must for auditors. It makes sense that management uses the high standard that their auditors will use in their internal review processes.
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.
Below is a comparison of MindBridge versus traditional audit analytics tools.
Traditional Audit Analytics Tools
Coverage of testing
|100% of known areas of interest||100% of known and unknown cases||MindBridge|
|Trusted, but outdated technology||Breakthrough technology, with growing evidence of its effectiveness|
(MindBridge positioned to surpass)
Knowledge curve (coding)
|100% of known areas of interest||Not required|
Ease of use (user interface)
|Cumbersome, requires repetitive, extensive user training||Highly visual, intuitive with little-to-no training|
|Time-consuming to set up rules for each client file||Not required|
|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|
|Rules are created to handle each case||Once flagged, machine learning automatically excludes them from future analysis|
|Built-in, automated “smart ingestion” leveraging machine learning|
|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|
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.
- when spending is expanding rapidly
- when revenues and spending are under pressure to contract
Budget cuts may sometimes be necessary
Audit committees and oversight authorities should consider their options carefully before the make a decision to cut internal audit. This services is important for several reasons, as internal audits:
- ensure accuracy of financial reporting — As organizations seek greater access to capital markets, the accuracy of financial reporting becomes particularly critical
- give assurance of efficiency and effectiveness — operational audits and performance audits are among the principal services offered by many government audit organizations
- foster greater accountability by shareholders and stakeholders — accountability over the effective use of scarce resources is vital
- find opportunities for cost reduction and containment — when forced to make difficult choices on which services to continue as revenues falter, your internal auditors are uniquely positioned to offer insights and perspectives to management
Most important of all, internal audit fosters good governance — especially when the effectiveness of board oversight is increasingly being questioned.
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.