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We need to do more than what corporate performance management has traditionally enabled to move from strategy to strategic actions while forecasting, monitoring, and managing performance.

What is Corporate Performance Management?

Undoubtedly not a new concept, corporate performance management is often referred to as business or enterprise performance management. Gartner defines corporate performance management or CPM as “an umbrella term that describes the methodologies, metrics, processes and systems used to monitor and manage the business performance of an enterprise”. And that is indeed a broad scope as business performance does touch several areas.

Corporate performance management isn't a matter of technologies or solutions or an isolated activity or strategy—the holistic perspective is crucial. Getting strategic, financial, analytical, and business knowledge is just one aspect of the equation that will help you make better business decisions.

The four crucial techniques linked to effective performance management are listed below:

• Analysis of the chance and choose the high-impact use cases to benefit from artificial intelligence
• Automation of tasks and establishing a long-term strategy to find quick wins for an all-encompassing change with automation
• Capturing of the value by streamlining jobs to manage organisational transformation and adopt an end-to-end process view.
• Integrating Artificial Intelligence (AI) into an operational model to make a transformational impact, analyse data to make wise decisions, and develop the capacity for ongoing improvement

How has AI impacted CPM?

The face of corporate performance management is changing due to artificial intelligence, among other things, in monitoring, reporting, analysis, forecasting, and analytical intelligence (as provided by analytical applications).

• The past corporate performance management only partially made it possible to foresee the future. But the present AI advancements help in real-time analysis, predictions, and future-oriented decision-making.
• With the help of artificial intelligence, pattern recognition, statistical forecasting, the analysis of vast amounts of market intelligence, decision-making can be made outside the traditional realm of historical information. When AI tracks KPIs and makes more data-based decisions, performance management can improve significantly.

Historically speaking, leveraging data has improved over time, opening more opportunities to learn from the past and present and, most importantly, to comprehend and plan using the proper KPIs. This is true within the broader context of strategy and planning in a corporate performance monitoring, reporting, and analysis environment.

To enable analysis, produce measurements, and changes, AI is integrated into the performance management process. Better performance management procedures can be sped up with artificial intelligence, increasing transparency and sustaining employee interest. Furthermore, corporate performance management enables better leverage of all data in the organisation’s ecosystem and helps to achieve its strategic objectives.

Advantages of a CPM Software

Corporations must adopt process automation in the age of business management intelligence. Here are a few advantages of using a CPM system.

1. Immediate feedback
Intelligent dashboards built into performance management software offer all the quantifiable metrics a management team could need to make decisions. The benefit, though, is not in the variety. It lies in the capacity to interpret and use data as changes occur in real-time throughout the entire organisation.
2. Data consolidation for easy management
The tools have the intelligence to gather, group, and combine data from multiple sources, be it departments, spreadsheets, or even companies.
3. Make risk management simple
The incorporation of tools like what-if models is a crucial benefit of CPM. By simulating the best- and worst-case situations, for example, the model equips managers to reduce risks and make wise decisions.
4. Offer primary data access and feedback
Managers may easily access information through performance management systems, promoting accuracy and quality.
5. Ease of collaboration
CPM tools are locally integrated and cloud-connected for all users to keep in sync across all departments.
6. Boost the standard of talent management
Recruiters and business leaders are now concentrating on employing AI-driven performance management to enhance the caliber of their hiring. It assists them in finding the appropriate talent pool and selecting the best candidates.
7. Effective data analytics for problem-solving
AI is a real game-changer in performance management systems and helps leaders and stakeholders make data-driven decisions and solve current challenges.
8. Developing a collaborative environment
Finally, businesses succeed when they concentrate on developing a collaborative environment where there is no place for hierarchy, bias, or inconsistent behaviour. AI in performance management systems will enable executives to spend more time on their employees and provide brilliant ideas.

What does Beinex offer?

STRACT by Beinex is an indisputably efficient tool that helps it deliver optimised business performance. It allows businesses to forecast, track, assess, and identify areas for improvement across all operations. Companies can thoroughly and comprehensively analyse all relevant financial and operational metrics across various levels of the business by consolidating data and performance metrics into one centralised database and then measuring this against their strategic goals.

STRACT is a comprehensive solution with features that allow you to create much more accurate and flexible budgets, enhancing your current business and resource planning and forecasting.

1. Start with Storage Using Amazon S3

Amazon S3 is a secure and reliable storage solution when you are dealing with massive datasets. It's highly scalable, extremely durable, and serves as a foundation for most data workflows. You can depend on it from initial data landing zones to backup archives.

2. Spin Up Power with Amazon EC2

When you need raw computing power for heavy-duty tasks, such as batch processing or running data pipelines, EC2 gives you the flexibility to choose instance types suitable for your workloads. You're in control of the compute environment, which is key for tuning performance.

3. Simplify ETL with AWS Glue

Managing extract-transform-load operations can be messy. AWS Glue resolves this with automated data discovery, code generation, and job orchestration. AWS Glue can support you if you're managing multi-source ingestion and need to clean and prepare your data for use.

4. Query at Speed with Amazon Redshift

Redshift offers the easiest and quickest way to run complex queries against large volumes of structured data. It's perfect for powering dashboards, reports, and business intelligence tools without the drag of traditional databases.

5. Tackle Big Data with Amazon EMR

If your workloads involve distributed computing using Apache Spark or Hadoop, EMR helps you deploy and manage those clusters in a fraction of the time. It is ideal for advanced data transformations and machine learning (ML) workloads, as it integrates easily with other AWS services.

6. Event-Driven Logic with AWS Lambda

Forget provisioning servers to process a few files. Lambda allows you to write lightweight, trigger-based code that responds to data events. It is an efficient serverless solution for processing files as they arrive or triggering downstream processes.

7. Streamline Real-Time Data with Amazon Kinesis

Modern data doesn't always arrive in neat batches; it streams in constantly. Kinesis helps you manage this chaos by capturing, processing, and analyzing real-time data. You can utilize it for use cases such as log monitoring, clickstream analysis, and sensor data processing.

8. Store Fast & Flexible Data with DynamoDB

DynamoDB is a fully managed, serverless database ideal for workloads where speed and uptime are paramount. It provides a NoSQL solution that works best in situations where low latency is essential, such as recommendation engines or personalized content delivery.

9. Keep Your Metadata in Check: Glue Data Catalog

The Glue Data Catalog can be considered as a metadata hub that consolidates information regarding datasets, schemas, and transformations for you. It improves discoverability and governance—two things no engineer should overlook.

10. Coordinate Workflows with AWS Step Functions

As you know, data workflows can span multiple tools, services, and dependencies. AWS Step Functions help you string those steps together into one cohesive flow, complete with retries and error handling. It's a visual way to orchestrate and manage complex processes with clarity and ease.

Best Practices for Using AWS Tools as a Data Engineer

AWS tools are powerful, but knowing what to use isn’t enough; how you use them is what drives real impact. That’s where the best practices for using AWS services come in:

• Scalability: Use services that grow with your data. Enable auto-scaling in EC2, EMR, and Lambda to handle variable workloads.
• Automation: Set up Glue jobs, Lambda triggers, and Step Functions to run tasks without manual effort.
• Security: Encrypt your data (both at rest and in transit) and adhere to least-privilege access with IAM roles.
• Cost Monitoring: Use spot instances, archive old data in S3 Glacier, and monitor costs with AWS Budgets.
• Smart Workflows: Break pipelines into smaller, reusable steps. Use Step Functions for clear orchestration.
• Track & Monitor Everything: Use CloudWatch and CloudTrail to keep an eye on performance, errors, and user actions.
• Organize Metadata: Keep your Glue Data Catalog updated and use clear naming so your data is easy to find and understand.
• Test Before You Trust: Validate your data and test your pipelines with sample loads before pushing to production.
• Document as You Go: You can easily maintain notes on your workflows, data sources, and transformations for smoother teamwork.

Wrapping Up: Why These Services Matter

Tools that enable speed, flexibility, and automation are not just desirable; they're essential. AWS offers a comprehensive toolkit that covers all stages of the data lifecycle. By staying up to date with these services, you not only improve your performance at work but also position yourself to take the lead in a data-driven, cloud-first future.

For data engineers seeking to excel in their roles, it is beneficial to become proficient in at least 10 AWS services. By serving as the foundation for scalable and effective data pipelines, these services help businesses transform unstructured data into actionable insights. Data engineers can significantly contribute to fostering innovation and informed decision-making within their companies by leveraging the potential of Amazon Web Services.

As financial systems become increasingly complex, fraud methods evolve accordingly. This blog covers the different types of digital banking fraud, including the fundamentals, emerging digital trends, global trends, and regulatory responses. Additionally, the blog highlights some of the most infamous bank fraud and financial crimes that reveal weaknesses in the financial system, serving as strong reminders of why constant vigilance in money is essential.

Understanding What is Bank Fraud

Bank fraud and financial crimes have affected economies worldwide, and the Middle East is no exception. From cyberattacks to fake loan applications, fraud comes in different forms. They target businesses, individuals, and financial institutions. Banking fraud includes deceitful practices designed to gain unauthorized access to money, financial assets, or confidential information, bringing huge financial losses to banks and damaging their reputation. Fraudsters are evolving, making it a necessity for banks to adopt proactive strategies in fighting financial crime. Several top-class fraud cases and money laundering scandals reveal weaknesses in banking regulations, which lead to fiscal instability and loss of trust. With the advances in technology, traditional fraud has taken new digital forms. Let’s examine the major types of digital banking fraud today.

Different Types of Digital Banking Frauds

Financial crimes in banking have become a significant concern, making it crucial for banks to adopt AI-driven technologies to tackle the threats. Let’s look at some of the different types of digital banking frauds: Identity Theft & Account Takeover: Fraudsters steal private information like credit card details, passwords, and social security numbers to get unauthorized access to accounts and make fund transfers and purchases. Mule Accounts & Money Laundering: Mule accounts are operated by money mules recruited by fraudsters or money launderers to transfer illicit funds while masking the identity of the true beneficiary. Scammers use mule accounts in the money laundering process to move money across different accounts, countries, or currencies, making it harder to detect. Phishing: It involves misleading individuals into disclosing sensitive information or executing specific actions that compromise their accounts. Fraudsters pose as legitimate organizations using emails, phone calls, or texts, creating a sense of urgency to prompt victims into action. Malware & Trojans: They are malicious software that, when installed on a customer's device, extracts confidential and private data. It enables fraudsters to control customers' online activities and access their devices remotely. Mobile Banking App Fraud: This happens when fraudsters create fake mobile banking apps imitating the real app to steal information. People usually fall into the trap of these fake apps by downloading from app stores or through phishing emails. Social Engineering Scams: It is a digital banking fraud that psychologically manipulates customers by tricking them into providing sensitive information through phishing emails, phone calls, or text messages that appear legit. There are different types of digital banking fraud, including online banking password theft, ATM skimming, digital wallet fraud, SMS, and text message fraud. Other types of financial fraud include mortgage fraud, loan scams, money laundering, employee fraud, Ponzi schemes, investment fraud, etc. While the above-mentioned digital fraud types are prevalent, fraud continually evolves, leading to new trends.

What’s Next? The Emerging Trends in Financial Crime You Need to Know

As new technologies emerge, fraudsters get smarter, and financial crimes evolve rapidly, becoming more sophisticated. Here are some of the key rising trends in fraud. AI-Generated Phishing: Cybercriminals harness AI to create persuasive phishing emails and messages, imitating context, tone, and communication patterns, making them even more difficult to detect. Deepfake-Enabled Scams: With the accessibility of deepfake technology, scammers now create hyperrealistic images, videos, and audio to impersonate bank officials and executives to authorize fake transactions, scheme employees into sharing confidential data, etc. Crypto-Related Frauds: Cryptocurrencies have opened up new roads to illicit financial activities like money laundering, crypto wallet thefts, etc., targeting beginners and seasoned investors. As much as AI helps prevent financial fraud, it also enables cybercriminals to handle such crimes. From automating attacks to tailoring scams to individual targets to evading fraud-detection systems, scammers could misuse the power of AI to extort huge amounts of money from financial institutions and individuals. However, AI can effectively serve as a critical line of defense for banks. Here are some examples of how AI helps prevent crimes and outsmart cybercriminals. Pattern Recognition: To identify anomalies and hidden fraud patterns by analyzing extensive datasets through machine learning models. Real-Time Monitoring: To detect unusual behavior and flag suspicious transactions faster. Biometric Authentication: To verify identities more accurately through voice ID, facial recognition, and behavioral biometrics. AI in KYC: To perform transaction monitoring, customer identification, and risk mitigation faster and more accurately using automated algorithms. Anti-Money Laundering, Driven by AI: To reduce false positives, detect patterns and anomalies in real-time, and boost compliance and risk management efforts.

Some Banking Scandals That Shook the System: Indicators Why Fighting Financial Crimes is Necessary

According to a survey by Visa, Dubai Police, and Dubai Economy (DED), 39% of UAE consumers reported being targeted by online fraud. Of these, 27% fell victim to phishing attacks, 19% experienced credit card fraud, and 17% were affected by counterfeit goods.
[Reference Link: https://www.arabianbusiness.com/industries/banking-finance/466063-cashs-popularity-subsides-even-as-online-fraud-rises ] Here are a few major bank fraud and financial crimes that happened in the Middle East. 1. A major private equity firm in the UAE collapsed in 2018 due to financial fraud. The investors' funds, including money for health projects, were misused, resulting in billions of losses, legal action, and increased control of private equity regulations in the region. Key Takeaways: • Financial transparency and accountability are paramount to building investor trust.
• Continuous auditing and meticulous review must be implemented to prevent mismanagement of funds. [Reference Link: https://www.bloomberg.com/news/articles/2019-08-07/what-s-been-learned-who-s-charged-in-abraaj-collapse-quicktake ] 2. A prominent business group in KSA orchestrated one of the largest financial frauds in the region, securing billions of dollars from the bank using fake documents and fraudulent loans. The scandal has sparked a legal battle and led to economic instability in the region, highlighting the importance of stronger risk management for lending practices. Key Takeaways: • Effective risk management is pivotal in preventing fraud.
• Implementing robust verification processes aids in comprehensively validating documents and loan requests. [Reference Link: https://www.news24.com/Tycoon-up-for-10bn-theft-20090718 ] 3. A huge corruption scheme worth 11.5 billion riyals was exposed by Saudi authorities. The scam involved bank officials, business people, and expatriates. The investigation revealed that the bank employees took bribes from an organized gang, which included fake commercial entities and accounts used to transfer illicit funds abroad. The scheme exploited bank positions and led to financial fraud, resulting in significant losses and damaging the financial system's integrity. Key Takeaways: • Financial systems must ensure transparency and accountability and comply with anti-money laundering (AML) standards.
• Banks must implement effective internal controls and anti-corruption measures to prevent fraud and ensure employees act in the institution's best interests. [Reference Link: Saudi Arabia: Massive fraud worth SR11.5 billion uncovered ] Strengthening bank surveillance, enforcing stricter conformance measures, and promoting corporate accountability are important to prevent future financial crimes in the region.

Regulatory Responses to the Surging Financial Crimes in the ME

Banks need strict regulations to prevent bank fraud and financial crimes, including Anti-Money Laundering (AML), enforcement of customer requirements (KYC), and increasing supervisory authority. Financial crime in the Middle East reveals a key gap between banking regulations and risk management. While governments and supervisory authorities are taking steps to improve transparency, fraudsters continue to find new ways to use the system. Here's a quick overview of some of the regulations in the region:

Central Bank of the UAE (CBUAE)

• Regulates banks, payment service providers, and finance and insurance companies at the federal level.
• Supports economic growth and promotes monetary and financial stability through effective surveillance, careful reserve management, and policy development aligned with global best practices.

Abu Dhabi Global Markets (ADGM)

• Regulates diverse financial entities, including asset managers, brokers, hedge funds, financial advisers, investment firms, and insurance intermediaries.
• Offers company registration and incorporation, different legal structures, regulatory support, and dispute resolution- all under a strong, advanced regulatory framework.

Saudi Arabian Monetary Authority (SAMA)

• Established robust regulations to safeguard KSA's financial sector's stability and security.
• Key areas include anti-money laundering, consumer protection, cybersecurity, risk management, anti-money laundering (AML), and consumer protection. Besides these regulatory bodies, banks in the MENA region must adhere to global regulations such as Basel III, Anti-Money Laundering (AML) laws, and Know Your Customer (KYC) requirements. They must also comply with international standards, including Counter-Terrorist Financing (CTF), Financial Action Task Force (FATF), and Basel Committee on Banking Supervision. Is your bank equipped to stand up to modern fraud threats? Get a FREE AI-powered fraud resilience assessment from Beinex and identify vulnerabilities- before fraudsters do! Start a FREE Assessment NOW!

Recognized as a leader in analytics and business intelligence platform for 9 consecutive years in Gartner Magic Quadrant, Tableau has a variety of options when it comes to analytics and visualizations and according to me, it keeps getting better and better with every update. The latest 2021.1 update comes packed with some impressive features and these are some of them that caught my eye:

• Einstein Discovery
• Quick LODs
• More Data Connections
• View Metrics
• Dashboard Extensions in Tableau
• Write to Excel in Tableau Prep
• Device Designer for Web Authoring
• License Improvements

Einstein Discovery After its acquisition by Salesforce, Tableau has been at the advantage of being able to leverage Salesforce capabilities. In this update, Tableau has introduced its integration with a powerful AI driven Salesforce product – the “Einstein Discovery”. With this feature, I can get smart predictive modelling capabilities for quick business decisions without any effort of creating complex codes or algorithm. Einstein Discovery has been rolled out in Tableau through three approaches, through dashboard extensions, through analytics extension, and through Tableau Prep which was released in version 2021.1.3. Although this feature requires an additional Einstein Discovery License, I find the end benefit totally worth it.

• Through Dashboard Extensions: To use “Einstein Discovery” in dashboards, I can just add an extension object on the dashboard and select Einstein Discovery. It will analyse the selected data and build predictive models. It shows me the top predictors in the view along with metrics that can be improved, and thus helping business decisions. This feature is available in Tableau Desktop, Tableau Server and Tableau Online.

• Through Analytics Extension: With this feature, I can directly embed predictions into Tableau calculated fields. All I have to do is go to Salesforce, use Model Manager to generate a Tableau table calculation script and paste the script into the calculated field in Tableau. This script then accesses predictive models in Salesforce by passing the data required for the model. This saves my time since I don’t need to create a code for the model. This feature is only available in Tableau Desktop and Tableau Server.

• Through Tableau Prep: This feature was released in 2021.1.3 and its ingenuity is worth mentioning. With “Einstein Discovery” in Tableau Prep, I can now use predictions while preparing data. I can bulk score my data and create fields to show prediction outcomes, to show top predictors and to show recommendations for improvement of the output.

Quick LODs LOD calculations are frequently used in Tableau to perform calculations on a fixed dimension. Where previously I had to mention the dimension to be fixed and the measures to be aggregated in a calculated field, now all I must do is select the dimension and measure in the data pane and right click to create an LOD calculation. Or even better, hold “Ctrl” to drag and drop the required measure field onto the dimension to be fixed. As simple as that. I can modify the calculation with more dimensions or change the type of aggregation later, if required. This feature is most beneficial as it reduces the chance of errors while creating complex LOD calculations, like misplacing a bracket, or adding any unnecessary characters by mistake. More Data Connections With the increase in popularity of Microsoft Azure services among users, Tableau has added multiple Azure connectors including Azure SQL Database and Azure Data Lake Gen 2 along with the existing Azure Synapse Analytics and Databricks. I can now connect to data that is stored in Azure’s SQL Database and Data Lake using the native Tableau connectors. Tableau has also introduced authentication to Azure services using the Azure Active Directory, making the connection more secure. In addition to this, four new data connectors have been added to Tableau Online and Tableau Server. They are Amazon Athena, Apache Drill, OData and SharePoint Lists. View Metrics A difficulty that I had faced with the metric feature was that I was not able to know the other metrics that were created in the same dashboard. With this update I can see all the metrics associated with a particular dashboard and modify these metrics if required or create new ones within the metrics pane. And previously, only if my site role were “Creator” or “Explorer”, I could see the metrics. But in this update, I can see all metrics connected to a dashboard irrespective of my site role. Dashboard Extensions in Tableau Earlier, to use an extension in a Tableau Dashboard, I had to drag and drop the extension object on the dashboard which would take me to its extension gallery web page and then I had to download it and then locate the downloaded extension on Tableau. Sounds tiring right? Not anymore! With this update, all I have to do is drag and drop the extension object onto the dashboard to select the required extension. I can now see all extensions and filter the list based on categories within the “Add an Extension” window in the Tableau screen. Write to Excel in Tableau Prep This is one feature that will prove useful to any regular Tableau Prep user. The days of exporting an output to csv and then converting it manually to an xlsx file are finally over. Using the latest version of Tableau Prep, I can directly export the output of a workflow into an xlsx file. Device Designer for Web Authoring Where before I could only generate device specific layouts in Tableau Desktop, now I can create custom phone or tablet views within the web itself. I no longer have to modify the device layout in Tableau Desktop and re-publish whenever a change in device layout is required. Licensing Improvements With this update, Tableau has introduced zero downtime licensing for the Tableau Server. Now for most of the tasks, whether it is the activation of a license or updating a feature that got added or applying some changes to user capacity, I no longer need to perform a server restart. So, these are some features that have been helpful to me and these are just a few of the lot. To view all the new features in 2021.1, you can view it at Tableau 2021.1 New Features In my view, Tableau has done a great job with its timely and inventive updates and seeing the rapid growth of AI and the increase of its application in numerous industries, I can predict for sure that Tableau will surprise the data science community with more innovative updates. Images Courtesy: Tableau