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Manage Online Financial Fraud Through Analytics

Mon, 25 Nov 2024 03:15:04 GMT

Fraud can happen to anytime anywhere. How can predictive analytics help businesses curb them. Find out!

Introduction

We often hear that " data is the new gold " in the modern age, and it’s abundantly available. But with its immense value, it’s no surprise that some may attempt to misuse it. Can we completely prevent such misuse? Probably not. However, we can detect it early and take proactive steps to minimize its impact. In this blog, we'll explore how modern data analytics can help reduce fraudulent financial activities, protecting both businesses' finances and their reputations. Let’s dive in!

What is Online Fraud

An activity/transaction, conducted online with the intent to steal identity, make illegal financial transactions, use stolen credit/debit cards to buy products and many more. Today, where every industry is loaded with crucial data, online fraud can happen anywhere, be it financial, health, supply chain, retail and many more.

What has changed

Technology is changing and evolving at the speed of knots, not our ability to prevent fraudsters to perform illegal activities. The world is going digital, with over 90% transactions in Australia going cashless. The graph below (Graph 2.1) shows the steep rise of digital payments (especially debit and credit cards) over the period of 2 decades. This is supported by improved and efficient internet infrastructure and technology rollout to accept digital payments to almost every vendor in Australia.

The above chart describes the rise of cashless economy and the decline in usage of cash . These tell us a lot about consumer behaviour not just in volume of transactions but also value of transactions.

How much fraud is happening

It shows that Australians made over 601,000 scam reports to organisations in 2023, an 18.5 per cent increase on 2022. In terms of financial losses, investment scams continued to cause the most harm ($1.3 billion), followed by remote access scams ($256 million) and romance scams ($201.1 million). With scam activity on the rise globally in recent years, the report highlights the impact of targeted and coordinated disruption activities across government, industry, law enforcement and community organisations, leading to lower overall financial losses.

*Source: ACCC

The results presented in this section show that individual losses associated with cybercrime victimisation vary widely. Most report losing no money from the most recent incident. Among those victims who do lose money and who could report how much, the majority lose less than $1,000. For some individuals, this may have little impact on their lives. For others, the impact may be substantial.

What is apparent from these results is that cybercrime is most frequently a high-volume, low-yield crime. While the methodology used in this survey does not allow for the results to be extrapolated to the wider community, the high rate of victimisation means that, even with the relatively small median losses per victim, the overall cost to Australian individuals is likely to be enormous.

Role of Predictive analytics in Fraud Management

Predictive analytics is a sophisticated approach to fraud prevention that utilizes data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data analytics. At its core, predictive analytics is about anticipating unknown future events, and in the context of fraud prevention, it plays a crucial role in identifying and mitigating potential fraudulent activities before they occur.

Using historical data analytics and user behaviour, predictive models discern patterns indicative of fraud. For example, a model trained on datasets marked with fraud instances learns to recognize and flag similar patterns in new incidents, whether they be in call logs, frequencies, or unusual user behaviours.

The true strength of predictive analytics lies in its continuous learning ability. By leveraging AI and machine learning, these models can constantly evolve and adapt, learning from past successes and failures to refine their detection capabilities, ensuring they remain effective even as fraudsters develop new tactics, staying one step ahead of the evolving threat landscape.

Using Predictive Analytics with BI Reporting

Imagine the ability to see probable fraudulent actions that can happen in future, and ability to slice and dice your data to see the consumer data, products, sellers etc all in a single report (Imagine your data set here). How powerful that reporting can be to prevent unwanted business losses, streamline business strategies and preserve business image in the market.

When businesses think this can't be achieved, or this is too difficult or it is too expensive of our business, we say TRY US! We will sit down with you and workout all the above issues to give your business the real power of data.

Why is Business Intelligence Important

Wed, 06 Nov 2024 21:48:39 GMT

Why do you need business intelligence in the age of big data? BI tools make data-driven decision-making a tangible, speedy reality

In this article, we’ll do a deeper dive on another question IT leaders will face - the “why” behind BI – specifically, why is it important?

Explaining the “why” of a particular technology matters greatly to any internal evangelism, especially during budget season or in any scenario where there’s cultural resistance to adoption or usage.

Benefits of BI

n BI’s case, this sort of evangelism often involves reminding people of its importance, since the discipline and corresponding technologies the term represents have been around for decades. In large enterprises in particular, BI has probably been around in some form for long enough that it goes relatively unnoticed, even by people who rely on it every day.

BI’s importance fundamentally boils down to this: It’s the practice of making data-driven decision-making – long the rage in business and other contexts – a tangible reality.

Data, workflows, and collaboration are intersecting, and whether it’s adjusting supply chains or deciding which product lines to focus on, it’s clear that intuition is nothing without data-based insight. Business Intelligence is poised to yield more value going forward with more data sharing between enterprises. The digital ecosystem is one that can grow in an open, collaborative environment, yielding more meaningful insights for the end consumer. This can be the patient in the healthcare ecosystem, the consumer walking into the retail store, the human with their device of choice, or the passenger in their transport of choice, etc. Culture is more often the barrier to the advancement of such collaboration than technology. The prevailing mindset of the data stewards across the extended enterprise will determine the value that Business Intelligence can provide going forward.

BI catalyses rapid decisions

The term “data-driven decision-making” doesn’t fully encapsulate one of its important subtexts: People almost always mean fast decisions.

This distinction matters because it’s one of the capabilities that modern BI tools and practices enable: Decision-making that keeps pace (or close enough to it) with the speed at which data is produced.

Data is now produced so fast and in such large volumes that it is impossible to analyse and use effectively when using traditional, manual methods such as spreadsheets, which are prone to human error. The advantage of BI is that it automatically analyses data from various sources, all accurately presented in one easy-to-digest dashboard.

Sure, everyone talks about the importance of speed and agility across technology and business contexts. But that’s kind of the point: If you’re not doing it, your competitors almost certainly are.

Take the guess work out!

There’s a difference between a gut call and a wild guess. BI is about making information readily available and accessible to the people who need it to do their jobs at any level of the organization.

The challenge is that we often have too much information. Moreover, it’s often incomprehensible in its raw form, or would require incredible amounts of time to sift through for value. This is the proverbial needle in a haystack – except the haystack gets bigger by the day.

BI done right presents information in a usable form that gets rid of a lot of the “hay,” allowing end users to actually utilize the data they’re presented with. This is why many tools and platforms place a heavy emphasis on dashboards and visualization as the primary UX/UI.

Business intelligence platforms with strong data modelling capabilities can turn disparate databases into a single source of truth that defines a company’s business logic. This can then be used across the entire company, so everyone is using the same language to represent critical KPIs and data.

BI &AI

A different form of technology-fuelled intelligence – artificial intelligence – has decidedly more star appeal than traditional BI. But BI and AI aren’t mutually exclusive; they’re increasingly intertwined.

The pairing of BI and AI can bring more automation to areas like data analytics and corresponding actions or responses to the analysis, for example, requiring less human intervention on more mundane tasks. This is similar to security automation and the automation of certain kinds of incident responses triggered by data analysis. The goal is similar, too: Increasing speed and improving outcomes in the face of overwhelming amounts of information.

Another way to think of it: AI should improve BI, not replace it. It’s ultimately concerned with similar business goals and challenges.