Posts Tagged “Data Analytics”

There is a common joke among physicists that fusion energy is 30 years away … and always will be. You could say something similar about artificial intelligence (AI) and robots taking all our jobs. The risks of AI and robotics have been expressed vividly in science fiction by the likes of Isaac Asimov as far back as 1942 and in news articles and industry reports pretty much every year since. “The machines are coming to take your jobs!” they proclaim. And yet, all of us here at Audit International still head to the office or log in from home each weekday morning.

The reality is less striking but potentially just as worrying. Most people expect that one day some sort of machine will be built that will instantly know how to do a certain job—including internal auditing—and then those jobs will be gone forever. More likely, is that AI and smart systems start to permeate into everyday tasks that we perform at work and become critical parts of the business processes our units and companies conduct. (Indeed, many professions and industries have already been greatly disrupted by AI and robotics.)

Technology companies have been so successful over the last 30 years because of the common mantra of “move fast and break things.” And that was maybe just about acceptable when it meant you could connect online to your friend from high school and find out what they had for breakfast or search through the World Wide Web for exactly the right cat meme with a well-crafted string of words.

When the consequences now might mean entrenching biases in Human Resources processes, or mass automated biometric surveillance, not to mention simply not even understanding what a system is doing (so called ‘black boxes’), the levels of oversight and risk management need to be much higher.

The Regulatory Environment :
There is some existing regulation which covers aspects of this brave new world. For example, in the European Union, article 22 of the General Data Protection Regulation (GDPR) on automated individual decision-making, provides protection against an algorithm being solely responsible for something like deciding whether a customer is eligible for a loan or mortgage. However, the next big thing coming to a company near EU is the AI Act.

The proposal aims to make the rules governing the use of AI consistent across the EU. The current wording is written in the style of the GDPR with prescriptive requirements, extraterritorial reach, a risk-based approach, and heavy penalties for infringements. With the objective of bringing about a “Brussels effect,” where regulation in the EU influences the rest of the world.

Other western jurisdictions are taking a lighter touch than the EU, with the United Kingdom working on a “pro-innovation approach to regulating AI,” and the United States’ recent “Blueprint for an AI Bill of Rights” moving towards a non-binding framework. Both have principles which closely match the proposed legal obligations within the AI Act, hinting at the impact the regulation is already having.

Much of the draft regulation is still being discussed, with a final wording soon to be agreed. There are disagreements across industries and countries on whether some of the text goes far enough or goes too far. For example, whether the definition of “AI” should be narrowed, as the current wording could encompass simple rules-based decision-making tools (or even potentially Excel macros) or even expanded to greater capture so-called “general purpose AI.” These are large models which can be used for various different tasks and therefore, applying the prescriptive requirements and risk-based approach of the AI Act can become complex and laborious.

The uncertainty over the final wording has given companies an excuse to not make first moves to prepare for the changes. Anyone who remembers the mad rush to become compliant with the GDPR will remember the pain of leaving these things to the last minute. The potential fines, which may be as high as 6 percent of annual revenue depending on the final wording, could be crippling and have a cascade effect on a company’s going-concern.

What Can Internal Auditors Do?
As internal audit professionals we can start the conversation with the business and other risk and compliance departments to shine the light on the risks and upcoming regulations which they may be unaware of. It is our objective to provide assurance but also add value to the company and this can be done through our unique ability to understand risks, the business, and provide horizon scanning activities.

Performing internal audit advisory or assurance work, depending on the AI risk maturity level at the organization, can highlight the good practice risk management steps that can be taken early to help when the regulation is finalized. These steps could include:

1) Identify AI in Use: To be able to appropriately manage AI risks throughout their lifecycle stakeholders need to be able to identify systems and processes which make use of them. Agreeing on a definition of AI and developing a process to identify where it is in use is the first step. This would include whether it is being developed in-house, is already in use through existing tools or services, or acquired through the procurement process.

2) Inventory: Developing an inventory which includes information such as the intended purpose, data sources used, design specifications, and assumptions on how and what monitoring will be performed is a good starting point and can be added to, based on your company’s unique characteristics and any specific legal requirements that are implemented in the future.
3) Risk Assessments: Since a key aspect of the AI Act is it being “risk-based,” it is important to have a risk assessment process to ensure you take the necessary steps as required in the regulation, based on the type of AI used. For example, what level of robustness, explainability, and user documentation is necessary based on the risk tier provided. It is also important to consider the business and technology risks of using the AI. For example, machine learning using neural networks requires large training datasets, which can raise issues of data protection and security, but may also perpetuate biases that are contained in the datasets. Suitable experts and stakeholders should be involved in the development and assessment of the risk assessment process.

4) Communications: One area that is often forgotten is communication. It is all well and good having a policy or a framework written down but if it isn’t known and understood by the relevant stakeholders it’s worth less than the paper it’s printed on. Involving key stakeholders during the development of your AI risk management processes can help develop a diverse platform of champions throughout the business who can act as enablers as the requirements are communicated and regulation finalized.

5) On-going monitoring: Risk management is not a one-off exercise and this is no exception. Use cases, technology, and the threat landscape change over time and it is important to include a process for on-going monitoring of AI and the associated risks.

The machines may not be coming to take our jobs just yet, but the risks are already here and so are the opportunities to get ahead. There may be a long and winding road in front, as we all prepare for a world where AI is commonplace and new regulations and standards try to shape its use, but each journey starts with a step and it’s never too early to get going.

“Audit International are specialists in the recruitment of Auditors and various Corporate Governance Professionals including Internal Audit, Cyber Security, Compliance, IT Audit, Data Analytics etc across Europe and the US.

If you would like to reach out to discuss your current requirements, please feel free to reach us via any of the following:
Calling
– Switzerland 0041 4350 830 59 or
– US 001 917 508 5615
E-mail:
– info@audit-international.com”

Here at Audit International, we have seen a significant shift in the way in which environmental, social, and governance (ESG) data has been perceived in recent years. It has gone from being an ‘add-on’ to being a vital opportunity for corporations to boost their competitiveness. As consumers become more discerning about environmental, social, ethical, and responsible business practices, organizations are increasingly starting to realize that reporting ESG data can have significant brand and reputational benefits.

However, this is just the beginning. The value of ESG data extends beyond reporting—when handled properly, it can unlock value for an organization in a variety of ways.

What is ESG and ESG Reporting?
It’s important to note that there is a distinction between ESG and sustainability. The terms are often used interchangeably, but there are important differences. Essentially, sustainability deals with how an organization’s operations impact the environment and society, whereas ESG has more to do with how an organization’s environmental, social, and governance initiatives affect its financial performance.

According to the Center for Audit Quality (CAQ), “ESG reporting encompasses both qualitative discussions of topics as well as quantitative metrics used to measure a company’s performance against ESG risks, opportunities, and related strategies.”

How companies can use ESG data to their advantage
When organizations treat ESG reporting as more than a box-ticking exercise to meet regulatory obligations, they stand to reap a number of benefits, as follows:

● Profitability and sustainability: Including ESG data in an extended planning and analysis (xP&A) strategy allows an enterprise to see how that data affects financial and operational data, which is key to making ESG initiatives sustainable and profitable.

● Risk management: Neglecting ESG issues can result in financial or reputational damage. Thus, all organizations should ensure that they incorporate ESG data into their risk management strategies. By voluntarily disclosing this information, they will demonstrate that they are taking sufficient steps to protect themselves and their stakeholders from ESG-related risks.

● Competitive advantage: Focusing on ESG can help an organization gain a better understanding of what matters to its stakeholders while also identifying opportunities. Furthermore, reporting ESG data will help stakeholders compare the organization with its competitors. This works in the organization’s favour if it is outperforming peers on the ESG front.

● Uncovering critical operational drivers for decision-making: ESG data can help an organization see where sustainable changes could improve efficiency and make its business more ethical and equitable. This can greatly enhance the decision-making process.

What are the main challenges to effective ESG Reporting?
ESG reporting is continuously evolving as governments announce new standards that companies need to comply with, as well as a new mandatory International Sustainability Standards Board (ISSB) standard that is expected to be announced by the end of the year (2022). It also touches every financial process. For these reasons, companies can find the whole ESG journey intimidating.

The following are some of the main obstacles that need to be overcome:

● Several ESG optional frameworks: The Global Reporting Initiative (GRI), Task Force on Climate-Related Financial Disclosures (TCFD), and the Sustainability Accounting Standards Board (SASB) are some of the more notable ESG frameworks, but there are plenty of others, many of which are specific to certain regions or industries. It can be challenging for companies, especially those operating in multiple countries, to know which ESG standards and frameworks to adhere to. This will all change when the mandatory ISSB standards are announced at the end of 2022.

● Complexity of data management: Whether meeting regulatory requirements or carrying out voluntary disclosures, companies need to be able to collect, translate, and process ESG data. This is a task that is complicated by the fact that the data is often siloed across different IT systems and is often stored in different formats. In addition, sustainability can be hard to quantify.

● Lack of ESG insight to inform decisions: Many organizations have difficulty seeing the connection between ESG data and financial results, especially when captured in spreadsheets, which means they are unable to use the data to improve their bottom line and sustainability initiatives.

“Audit International are specialists in the recruitment of Auditors and various Corporate Governance Professionals including Internal Audit, Cyber Security, Compliance, IT Audit, Data Analytics etc across Europe and the US.

If you would like to reach out to discuss your current requirements, please feel free to reach us via any of the following:
Calling
– Switzerland 0041 4350 830 59 or
– US 001 917 508 5615
E-mail:
– info@audit-international.com”

Amidst issues like supply chain complexity, economic uncertainty, and increased digitalization, Audit International are finding many organizations are adding vendors or changing their existing relationships with those they currently conduct business with.

Working remotely has prompted many companies to add cloud vendors. Supply chain backlogs might have prompted your business to switch to local vendors. Or maybe you’ve added marketing agencies or other types of consultants that have flexible capacity, rather than increasing headcount.

These decisions can help businesses adapt to changing conditions and build resilience, but working with vendors may also introduce new risks. While you might feel like you have a handle on issues like in-house data security processes, you need to be sure that vendors also align with your needs in these areas.

Internal audit teams can play an important oversight role when it comes to vendor risk management. While they might not be making specific vendor management decisions, they can still be involved in making sure proper due diligence is followed when selecting vendors. And once vendor relationships are in place, internal audit teams can monitor these arrangements to ensure organizations aren’t opening themselves up to new risks.

What are the top vendor risk management issues?
Working with third parties like software vendors, managed service providers, cleaning companies, etc. can help businesses fill gaps in current capabilities, increase efficiency, and more. Yet, internal audit teams also need to make sure that their organizations are accounting for any and all potential risks:

Cybersecurity: Internal audit teams should review vendors’ cybersecurity practices to assess whether these meet your organization’s expectations, for example, data security controls and remediation capabilities.

Compliance: Third-party vendors can also create compliance risks, such as improperly storing customer data or engaging in illegal business practices. Even if these vendor issues do not lead to legal action against your organization, internal auditors should aim to get ahead of these issues to avoid reputational damage.

ESG: Environmental, social, and governance (ESG) scrutiny is increasingly extending into supply chains and can also create reputational risk. Internal auditors will want to assess how vendors align with their own ESG goals. This may in turn lead to implementing additional controls, for example, around data sharing practices so that your organization will be able to verify issues like vendor emissions.

Quality: Don’t automatically assume that vendors will provide the quality you’re expecting, even if they come recommended or are widely known. Internal auditors need to ensure that their organizations still conduct proper due diligence to see whether working with that vendor will provide the quality of work you’re expecting. Managing risk can also include looking at vendor performance controls to see if existing third-party vendors maintain appropriate quality standards.
These are just some of the many critical risks that can come from working with third parties. Keep in mind that vendors may also have their own networks of third parties, which could ultimately affect your organization.

While it might not be possible to know every connection point that your vendors have with other third parties, you would likely want to assess what their own third-party risk management practices look like.

How can internal auditors improve third-party risk management?
Internal auditors shouldn’t be the only ones responsible for vendor risk assessments, but they should be mindful of the aforementioned vendor risk management issues and collaborate with other departments to stay on top of these risks.

For example, internal auditors can collaborate with IT leaders to create a vendor security due diligence checklist. From there, internal audit controls can make sure that this checklist is used across all vendor reviews.

Internal audit leaders can also integrate analytics into audit processes, such as collecting performance metrics on third-party vendors, to assess whether they meet your organization’s quality expectations on an ongoing basis.

Too often, however, adding analytics to audit reports is a manual, labor-intensive process that can create its own risks, like data errors. TeamMate Audit Benchmark found 79% of internal audit teams manually leverage data from other applications.

Audit tools like TeamMate+ can help internal auditors get the third-party data they need through automated API exchanges with other platforms, which makes continuous monitoring of risk more feasible. They can then create automated reports to share insights with other departments to stay on top of third-party risk.

By aligning with these steps and staying on top of evolving vendor management risks, internal audit teams can help their organizations stay safe while getting the most out of their third-party partnerships.

“Audit International are specialists in the recruitment of Auditors and various Corporate Governance Professionals including Internal Audit, Cyber Security, Compliance, IT Audit, Data Analytics etc across Europe and the US.

If you would like to reach out to discuss your current requirements, please feel free to reach us via any of the following:
Calling
– Switzerland 0041 4350 830 59 or
– US 001 917 508 5615
E-mail:
– info@audit-international.com”

At Audit International, we know when people hear buzzwords like ‘data analytics’, ‘artificial intelligence’ and ‘machine learning’, it can be intimidating. Many people don’t fully understand such concepts, but in truth, you don’t need to. You just need to get comfortable with them. And you probably already are: familiar services like Netflix or Spotify use artificial intelligence to understand your preferences and make subsequent suggestions based on that knowledge. The level of consumers’ expectations is continually increasing, and the successful companies are those that are advancing with technology. The same is true for businesses and their expectations. In audit, the revolution is underway and the sections that follow highlight the key drivers for this change.

Improve the audit experience –

The volume of data available to auditors is astounding, but in most cases, this data is simply not being used. If this were happening in any other industry, there would be questions to answer. Data analytics can improve the audit experience in several ways, for both the audit team and for the client.

Improve audit quality-

During the planning phase of the audit, audit teams must shift their focus away from the old mindset of “what could go wrong?” Through analytics, we can turn our attention from what could go wrong to what has gone wrong. Auditors have access to the client’s complete financial data for the period under audit – if they focus on analysing and understanding the data, they could identify an unexpected transaction or trend in the process. During the execution phase, auditors should also build on the knowledge gained in planning to truly understand the business in question and focus their attention on higher risk transactions. Finally, auditors should move away from a ‘random sample’ approach and, instead, focus on the transactions that appear unusual based on their knowledge of the client, business or industry. These are just a few areas where improvements in audit quality can be achieved using data analytics.

Improve efficiency-

In the examples above, the use of data analytics in planning will identify what has gone wrong and any associated unusual transactions. In execution, these transactions will be tested as part of the audit sample. It could also cover some requirements under auditing standards concerning journal entry testing, as the journal entries will likely be the data that highlighted what went wrong in the first place. Again, this is just one example of efficiencies gained without even considering the hours saved by automating processes like creation of lead schedules and population of work papers.

Post-pandemic world-

The world will be a very different place in years to come. Firms with the ability to perform in-depth analysis using data analytics undoubtedly have a significant advantage over those that do not, given the efficiencies they can gain and the potential reduction of physical evidence required from clients, among other things. Due to the changes we have all had to endure, auditors may also have additional procedures to perform (e.g. roll-back procedures where they were unable to attend stock counts at year-end due to the COVID-19 closures of businesses). Such procedures have the potential to be automated, saving even more time and effort for audit teams.

Improve engagement-

Rather than spend time performing mundane tasks such as testing large randomised samples, data analytics allows audit teams to jump into the unusual transactions. This will make the job more interesting to auditors and cultivate a curious and questioning mindset, which will, in turn, lead to improved scepticism and audit quality.

Improve client experience-

This might happen in two ways. First, the time saved by the client’s staff (who, in theory, will have fewer samples for which to provide support) and second, through the value the audit adds to the business. As an example, consider an audit team performing data analysis on the payroll for their client. As payroll is a standardised process, the audit team has an expectation around the number of debits and credits they would see posted to the respective payroll accounts each month. As part of their analysis, however, they find an inconsistent pattern. This can be queried as part of the audit and the client will be better able to understand a payroll problem, which they were previously oblivious to.

Client expectations-

Given the level of data analysis that occurs daily in the life of anyone using a smartphone, a consistent, high quality is understandably expected in people’s professional lives, too. Audit clients, like all consumers, want more. They want a better and faster audit. They want an audit that requires minimal interference with the day-to-day running of their business, without compromising the quality of the auditor’s work. With troves of data now available to auditors, such expectations are not entirely unreasonable. Audit firms have access to vast amounts of financial and related data – in some instances, millions of lines of information – that, if analysed robustly and adequately, would improve their processes, their clients’ experience, and the quality of their audit files.

Aspirations of professionals-

Audit professionals can often struggle with work-life balance, as we here at Audit International know. Though most firms are getting on top of remote working, the hours in busy season are long. In a time of continuous connectivity, the time frame around ‘busy season’ is also becoming blurred. Through the use of technology, we will one day make auditing a ‘nine to five’ job. Many will scoff at that idea and, although we do not expect this to happen in the next five years, or even ten years, it is possible. By automating mundane tasks and continuously upskilling our graduates, we can transform how an audit team completes work. There will be more scope to complete work before clients’ financial year-ends, thus moving much of the audit out of the traditional ‘busy season’. Machines can complete specific tasks overnight so that auditors could arrive at their desk, ready to work on a pre-populated work paper that needs to be analysed by a person with the right knowledge. With appropriate engagement by all parties (i.e. audit teams, senior management, and audit clients), we could significantly reduce the hours spent on audit engagements and give this time back to auditors. Along with attracting high-calibre graduates, we will retain high-quality auditors in the industry while also avoiding mental fatigue and burnout, which will again lead to better quality audits.

Graduate recruitment-

Graduates joining firms in recent years have particular expectations of the working world. They want job satisfaction, flexible hours, remote working, and an engaging role that will challenge them. Professional services firms have to compete for the very best graduates, and no longer just against each other – a host of technology-enabled businesses are attracting talent on an unprecedented scale by meeting the needs listed above. Technology, and data analytics, in particular, can offer the solution to the graduate recruitment challenge – by making the work more efficient and automating mundane and repetitive tasks, graduates can instead focus on analysis. Time and time again, when we talk to candidates, we always hear that if they find their work challenging and interesting, they will feel more engaged.

Challenges-

This move towards technology is not without its risks to the profession. Automating basic tasks removes the opportunity for graduates to form a deep understanding of these sections of the audit file. The onus is therefore on the current cohort of Chartered Accountants to take the reins, both to drive technology advancement forward and also provide practical, on-the-job coaching to ensure that this knowledge is not lost for the generations that follow.

“Audit International are specialists in the recruitment of Auditors and various Corporate Governance Professionals including Internal Audit, Cyber Security, Compliance, IT Audit, Data Analytics etc across Europe and the US.

If you would like to reach out to discuss your current requirements, please feel free to reach us via any of the following:
Calling
– Switzerland 0041 4350 830 59 or
– US 001 917 508 5615
E-mail:
– info@audit-international.com”

The job profile of the Data Scientist is still young, but is often searched for on the job market. They are required in many industries, such as:

• Banking and insurance 
• Trading
• Business and organizational consultancies, market researching
• Social Media, Telecommunications, online tradinging and network management
• Bio-, pharmaceutical, chemical and medical industries
• Logistics
 

 
In 2012, Tom Davenport, Professor at the Harvard Business School, has described the competence profile as following: „… a hybrid of data hacker, analyst, communicator, and trusted adviser. The combination is extremely powerful – and rare.“
In times of “big data”, Data Scientists are experts in demand, who are paid above average and enjoy great freedom in companies as “gold diggers”. Using methods of mathematics, computer science and statistics, they gain facts and knowledge from large amounts of data, the “gold of the 21st century”, and discover new business areas. In addition, they are something like interpreters. They formulate the data records into legible results and display the essential information in a comprehensible language.
Data Scientists are trained in statistics, graph theory and other mathematical fields, and are proficient in methods such as data mining, process mining, machine learning and natural language processing (NLP). Added to this is knowledge from practical computer science. Knowledge of operating systems, databases, networks and data integration tools, as well as the most important programming languages and analytics tools are mandatory. Furthermore, knowledge about the Hadoop ecosystem, social networks and other systems from the internet and big data environment is a compulsory requirement for professional practice. The competency profile is that of an all-round talent and accordingly (currently) difficult to find.
 
The Data Scientist and the financial function within the company
The question whether a controller can assume the tasks of a Data Scientist must be clearly denied in the context of the described competence profile. The current opinion in the industry is, that it is illusory to believe that controllers could also assume the tasks of a Data Scientist. However, controllers should know the job profile of a Data Scientist as well as the possibilities and limitations of Big Data. The cooperation between the tasks of a controller and a Data Scientist is an important source for the future economic success of companies.
 
The Data Scientist and Auditing
The advancing digitization also places new challenges on internal auditing in the selection of the audit methodology. Data Science offers the possibility to consider the analytics of data masses as a test step within an audit and in this way to create an additional benefit. This means, however, that the internal audit department must also acquire expertise in data science in addition to the already acquired competences, such as finance, business management and compliance. Since an individual auditor can hardly have all the competences mentioned above, these should be at least available within the team. If necessary, remember to include an external Data Scientist.
Along the lines of internal auditing, the external auditing is placed before conditions that were changed by digitization: the flood of data, the appropriate audit methods as well as the concern of finding young recruits within the auditors underline the need for efficiency gains. The surge in job advertisements for data scientists in audit centers, as well as first attempts to use artificial intelligence in this area, underscores this.

This feature blog was written by Prof. Dr. Nick Gehrke (Zapliance)

One of the new hottest tech trends for the IT Audit Market and in particular the Data Analytics Market is artificial intelligence (AI). As the consumption of data persists and the growth continues with no apparent limits, we see companies across the globe are investing not only in big data analytics hardware and software but more so in the people with the skills and knowledge to utilises them. They are also heavily investing in these hard to find individuals’ continuing education.
According to a recent article released by Forbes, the world’s biggest companies such as Google, Facebook, IBM and Amazon are heavily investing in hiring and acquiring new IT talent in the arena of data analytics.

This in turn will hopefully lead to the development of new tools for migrating data, and also analysing. The predications for the market are estimated to grow in excess of €200 billion so individuals with expertise and knowledge in these specialised fields and areas are certain to build a successful career with unlimited career options and growth.

This growth will also apply not only to individuals but also to the information-based products which are also set to increase with Fortune500 companies investing heavily in new technology and infrastructures for information gathering. The data acquired can then be either bought or sold which again will lead to further revenue generation.