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Bridging the Gap: the crucial role of last mile data integration in financial services

Financial firms worldwide are striving to achieve last mile data integration, a process that seamlessly integrates data into business workflows and puts it at the disposal of business users. The goal is to eliminate the need to search through databases or data warehouses for required data, allowing easy access for reporting and financial models, and enabling better decision-making.

By Martijn Groot, VP Marketing and Strategy, Alveo

By Martijn Groot, VP Marketing and Strategy, Alveo
By Martijn Groot, VP Marketing and Strategy, Alveo

Financial services firms spend material amounts on acquiring and warehousing data sets from enterprise data providers, ESG data companies, rating agencies and index data businesses.

However, when this data is not readily available to business users or applications where it impacts decisions those investments will not deliver the return they should be. For many financial services businesses, last mile data integration represents a missing link in ensuring they are optimising the value they obtain from data. The volume of data they need is continuously growing and the bills they face for acquiring it are therefore going up in tandem.

Activating data assets

Ultimately, firms will not get the best out of their investment in data, if they don’t have a way, first, to verify it, and second, to land it into the hands of their users or enable users to self-serve. If the data is conversely, still sitting in a database that is hard to get to, or needs skills to access, then the business will not achieve maximum value from it.

That in a nutshell is why last mile data integration is so important to them. Achieving it does however come with challenges.  Organisations must establish efficient data onboarding processes and transform data sets to meet diverse technical requirements common in their applications landscape. Additionally, maintaining high service levels and responsiveness to requests for new data to be onboarded is vital to build trust and keep business users engaged.

So how can all this best be achieved? The key is efficient data management. To use an analogy, financial data management can be seen in the context of the human body, with the need to manage data flows analogous with the circulation of blood through the arteries. Data gushes in from internal and external sources.

It needs to be cleaned and a process of data derivation and quality measurement applied and then we see the end result in the form of validated and approved data sets.  The overall flow often stops at that point for financial services organisations. But such an approach is incomplete in that it actually ignores last mile data integration. Data may be flowing through the arteries of the organisation but it is not reaching the veins, and capillaries.

That’s where the key step of distribution comes in. This not only enables easier access to the data in whatever format required by lines of business within the organisation but also to set up exports or extracts of relevant data in predefined views or formats that then flow easily into business applications.

Maximizing data ROI

Financial sector organisations understand the need to do this but often they end up doing it in a way that involves a lot of ad hoc manual maintenance at the individual desktop level, which means that process get out of sync; data becomes stale and there is the danger of duplication. All this inevitably ends up impacting the quality of decision-making also.

Effective last mile data integration is an automated process that involves identifying relevant data sources, mapping and cleaning the data and then transforming and loading it into the target system and using data quality and consumption information in a feedback loop. The key to this process is making it easy for the specific business user. It is about understanding the kinds of taxonomies and nomenclature the user is expecting and then being able to mould, build and shape the data being presented in a way that best suits that user.

Financial services firms that get all this right will be well placed to unlock the full potential of their investment in data and maximise the ROI on the data they purchase. Ultimately, by delivering on this process and verifying and making data readily available to users, organisations will put themselves in the best possible position to make informed decisions, streamline operations, and position themselves for ongoing success.

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Embracing technology to navigate economic turbulence in the financial services sector

Guy Mettrick, VP, Financial Services at Appian
Guy Mettrick, VP, Financial Services at Appian

Today’s dynamic financial landscape has exposed the vulnerabilities of the financial services sector and shattered preconceived notions about banks’ regulatory resilience. The rapid collapse of once-revered institutions highlights the fragility of the banking sector in the face of economic turbulence and unforeseen market shifts.

With analysts scrambling to dissect the factors behind these failures, it is crucial to consider the broader implications for the financial services industry and the potential ripple effects on the overall economy.

Guy Mettrick, VP, Financial Services at Appian

Adaptive strategies for growth and innovation are becoming increasingly important amidst a background of stricter risk management, reduced lending, and increased regulation. To navigate the unpredictable path ahead that is defined by tightening regulatory frameworks and resource limitations, agility is key.

Balancing regulatory challenges

Mounting regulations driven by factors such as climate change and the push for enhanced compliance are forcing businesses leaders to reconsider their organisation’s strategic approach. The prominence of environmental, social, and governance (ESG) objectives in the financial services sector requires increased attention and significant investments in human resources and technology.

While these circumstances may lead to scaled-back growth aspirations, cost-cutting initiatives and deferred investment decisions, they also present transformative opportunities.

Leveraging technological advancements

During economic uncertainty, technology emerges as a powerful force within the financial services landscape. When it comes to expediting client onboarding, enhancing customer service, and facilitating seamless communication between financial institutions and their clients, automation proves indispensable. Automation enhances process efficiency and efficacy by eliminating manual tasks and minimising errors. Advanced technologies like artificial intelligence, robotic process automation, and process mining empower financial organisations to drive innovation within complex frameworks.

With automation, firms can facilitate real-time reporting and audits that provide tangible evidence of control effectiveness by embedding risk controls directly into their processes. In an era of increasingly stringent regulatory frameworks, this proactive approach to compliance proves invaluable.

The rise of data fabric

One emerging trend is the adoption of enterprise-wide data fabric, project by Market Watch to grow from $1.71 billion in 2022 to $6.97 billion by 2029. Data fabric streamlines the consolidation of data from various systems, a process that has traditionally been challenging and costly. This integration eliminates the need for data migration – a critical prerequisite for successful process automation.

Data fabric seamlessly connects and harmonises existing databases. This breaks down data silos and enables a cohesive and compliant framework that consolidates all relevant data sources. Within the financial services sector, this technology facilitates easy access to vital components such as risk governance policies and customer data.

Financial service providers must adopt adaptive strategies and embrace technology to effectively manage risks, regulations, and growth during an economic downturn. Regulation should not be perceived as a burden. Financial institutions should view technology, particularly process automation, as a catalyst for growth. Automation and data fabric enable these organisations to navigate complexities, streamline operations, and enhance customer experiences. Rather than succumbing to challenges, financial service providers can leverage technology to foster innovation, ensuring resilience in the face of economic uncertainty.

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eCommerce is no longer possible without web scraping and big data

Gediminas Rickevičius, VP, Global Partnerships at Oxylabs
Gediminas Rickevičius, VP, Global Partnerships at Oxylabs

Studies have shown that companies that use data are almost 20 times more likely to be successful and have more than 50% better understanding of their customers. As a result, web intelligence is becoming increasingly important for businesses that rely on data, particularly for platforms that use publicly available data to analyze competitors, track customers, and generate leads.

By Gediminas Rickevičius, VP, Global Partnerships at Oxylabs

Web scraping and big data are essential for any eCommerce business allowing companies to glean insights from their competitors and provide the most up-to-date information on pricing, promotions, and market trends.

Changes in the retail landscape

In the US, the number of traditional retail stores dropped from over 450 thousand to nearly 350 thousand in 2021, with only a slight 2% increase in 2022. Although brick-and-mortar shops are slowly recovering after Covid, the increasing rent prices and cost of living are bringing new challenges to these businesses. It is estimated that over 50% of sales this year will be processed through digital platforms, ensuring the long-term viability of ecommerce.

The shift to online shopping revealed the need to get to know the growing number of customers better and faster. Competitiveness will only continue to grow, forcing companies to collect as much information as possible. Often it is understood that more data means a stronger business.

Big data – the driver of eCommerce competition

With the rise of accessible analytics tools and data-driven marketing strategies, eCommerce companies now have the advantage of tracking customer behavior more accurately. As a result, they are better able to tailor their services and products to meet customers’ exact needs and outplay their competitors in the process.

In the ecommerce world, big data is driving competition in a number of ways. By understanding customer behavior and preferences, retailers can better target their marketing efforts and personalize shopping experience to increase conversion rates. Additionally, companies can utilize advanced analytics to identify patterns and trends that can give them a competitive edge.

Data is also changing the landscape of pricing in eCommerce. Real-time data enables retailers to track competitors’ prices and adjust their own to stay competitive. Furthermore, dynamic pricing algorithms that take into account a variety of factors are becoming more common, further removing traditional price barriers.

Getting to big data with web scraping

Every day, approximately 2.5 quintillion bytes of data is created, and this deluge of information can be overwhelming for businesses, but it also presents a unique opportunity. Those who are able to harness this data and use it to their advantage will be well-positioned to succeed in the ecommerce competition.

Companies can make sense of this abundance of data and turn it into an advantage by creating a map of their competitor’s ecosystems. This involves not only identifying direct competitors but also analyzing their relationships with other players in the market.

Web scraping allows companies to quickly gather data about competitors’ assortments, observe what new products are appearing and disappearing, monitor price changes, and from that, observe their competitor’s strategy and learn. All this information can then be used to create a map of the competitive landscape, which can be valuable for a variety of purposes, such as:

Market trends analysis. Allows analyzing the introduction of new products and technologies, changes in market conditions, and shifts in customer preferences. By staying abreast of these changes, businesses can adjust their strategies to stay competitive and take advantage of new opportunities.

Competitive intelligence. A competitive ecosystem map can help a company to stay informed about its direct competitors, suppliers, as well as any other companies that might be vying for their customers’ attention.

Strategic planning. A competitor ecosystem map allows businesses to visualize the competitive landscape and better understand their competitors. This involves not only identifying direct competitors but also analyzing the relationships between competitors and other market players, such as suppliers, distributors, and customers. This can help businesses identify potential new partners, suppliers, and customers, as well as potential new threats.

By having a comprehensive understanding of the competitive landscape, companies can develop strategies to expand their market share.

Conclusion

eCommerce companies can no longer afford to operate without web intelligence and big data. These information sources are essential for staying competitive in today’s digital marketplace and for making data-driven decisions that will drive growth and profitability.

The competition relies heavily on the availability and utilization of data. A superior understanding of the information gives a permanent and comprehensive edge to a player. When one participant gains this advantage, the others must also adopt it to remain competitive. Otherwise, they will eventually be at a disadvantage in the long term.

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Banks have the Generative AI advantage, but must overcome challenges to fully utilise its benefits

Jay Limburn, VP of AI Product Management, IBM
Jay Limburn, VP of AI Product Management, IBM

Despite the many challenges the industry has faced, the banking sector has continued to prioritise digital transformation and it is only accelerating quicker. Generative artificial intelligence (AI) is the latest in a wave of disruptive technologies that will drastically transform the financial services and banking industry.

By Jay Limburn, VP of AI Product Management, IBM

Many banks and financial institutions are as good as, if not better than most industries when it comes to technological maturity. We have been working on generative AI with banks for several years, and they have been experimenting with the operational advantages of AI across their business. The IBM 2023 CEO Decision-Making in the Age of AI report showed that 75% of CEOs surveyed believe the organisation with the most advanced generative AI will have a competitive advantage. However, executives are also concerned about the potential risks around security, ethics and bias.

Leaders are looking to fuel their digital advantage to drive efficiencies, competitiveness and customer satisfaction, but they have not been able to fully operationalise AI as they face key challenges around implementation.

The biggest challenge and opportunity…data

Banks are continuing to digitally innovate, and data has emerged as one of the biggest challenges to fully utilising generative AI across the industry. Platforms like ChatGPT caught people’s imaginations and created excitement in the sector. But while they rely on Large Language Models (LLM) to analyse vast amounts of data, the banks need to be able to choose from multiple models and embed their own data sets for analysis.

Instead of having one model to rule them all, banks will need to evaluate which models can be applied to their individual use cases. Banks are aware of the benefits generative AI can bring, so in place of summary capabilities of what the technology can do, they need to look at how to modernise different elements of their business. This requires models to be trained on the bank’s own data sets to get high-level accuracy and to fully operationalise the technology.

The amount of data is overwhelming many organisations, and banks are not excluded. To succeed, financial institutions will need to embed their own data into generative AI models to fully operationalise the technology.

Banks can help shape regulation and governance

One of the other key challenges facing banks with regards to generative AI is regulation and governance. As a new and emerging technology, regulators will not necessarily understand AI, so the natural inclination is to say we cannot use it. Equally, some models cannot explain why it has made a decision. For trust and compliance, financial institutions need to explain their decision-making process.

The more AI is embedded into organisations, the more important it is that leaders have a proactive approach to governance, which means having a legal framework to ensure AI is used responsibly and ethically, helping to drive confidence in its implementation and use.

But in order to help shape the AI regulatory environment and meet these requirements, banks need to take an active part in shaping the regulatory framework and move to models which can explain the decision-making process.

Generative AI will help not lead

The response we have seen from banks to generative AI has been phenomenal. As an industry, financial services and banking can lead the charge around AI regulation and explore new models to leverage their own data for better outcomes.

However, this isn’t without its challenges. Operationalising generative AI has proved difficult due to potential risks, compliance and evolving regulatory requirements, and concerns would be heightened as banks introduce their own data to AI models – which is why most generative AI use cases have so far focused on the customer care space.

Despite these challenges, banks have a huge opportunity to leverage generative AI, which will fundamentally change how we bank and how banks serve customers, and governance will play an active role in ensuring trust as we continue to explore the benefits of generative AI. Importantly, AI is here to help banks, not be the lead in most use cases.

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Small Finance Banks – The quest for technology-led differentiation

Since their inception, Small Finance Banks (SFBs) have been primed as a vital cog for the last mile credit and service delivery for the MSMEs, farmers, and unorganized sector units, helping to bridge the $240 billion credit gap for the underserved segment.

Naveen Gupta, Senior Product Owner, Tagit
Naveen Gupta, Senior Product Owner, Tagit

By Naveen Gupta, Senior Product Owner, Tagit

These Small Finance Banks have a robust base of borrowers with small credit needs. The banks so far have been reasonably successful in serving their priority segment and are now looking to establish their presence in the commercial banking space by evolving beyond a credit-only institution to a diversified financial institution.

In today’s environment, SFBs are facing twin challenges. Where, from one end, the FinTechs are grabbing their market share using innovation and new technologies and at the other end incumbents’ banks are blocking their market access with their size.

To compete with them, SFBs must step up their game. They need to look beyond rate strategy (providing higher interest rates on CASA and deposits as compared to the incumbent banks) and build a robust, sustainable differentiation built around their primarily intended high-technology, low-cost model.

Born on the cusp of the digital era, Small Finance Banks do not come with the baggage of legacy technology. Though they don’t have the capital to match the technology spends of their incumbent peers, the unbundling of the banking technology stack and ecosystem driven collaborative innovation – courtesy API economy and open systems – presents a great opportunity for them to undertake a phased, yet fast leap towards digital transformation, all the while keeping IT spends under control.

SFBs must focus on:

  1. Implementing digital channels for banking services: Banks can use digital platforms such as mobile apps, online banking portals, and social media to provide customers with convenient and secure access to their accounts, transactions, and other banking services.
  2. Enhancing security: Banks can use advanced security measures such as biometrics, encryption, and multi-factor authentication to protect customer data and prevent fraud.
  3. Partnering with fintech: Banks can collaborate with fintech companies to access new technologies and innovative products and services to enhance their digital capabilities.
  4. Investing in digital infrastructure: Banks can invest in modernizing their IT infrastructure to enable better data management, improved scalability, and enhanced security.
  5. Providing digital financial education: Banks can use digital platforms to educate customers about financial literacy and digital banking services.
  6. Improving data management: Banks can use big data and analytics to gain insights from customer data and use it to improve product offerings, target marketing, and personalize the customer experience.

With the right technology transformation strategy powered by smart investments and careful roadmap considerations, Small Finance Banks can grow their business and achieve sustainable differentiation while keeping costs under check.

Banks need to ensure that they have the right partners for their digital transformation. Partners having plenty of digital transformation experience in the Indian market can help transform SFBs with the right speed and scale without impacting existing business and thereby enabling the SFBs in their journey of expanding market share and revenue.

Banks should collaborate with Digital transformation partners like Tagit who have platform-led solutions, provide more value in the long term, ensure that solutions are future-ready, and services delivered are secured and scalable.

With the right mix of products, SFB can successfully transform to a universal bank, increasing their market presence fending competition from new age fintechs and other banks and bringing more value to their stockholders. Tagit can help Small Finance Banks in increasing their customer base and revenue and enhancing customer loyalty with new and innovative features.

Tagit has been helping banks in India in their digital initiatives by providing best-in-class digital solutions alongside a holistic digital roadmap.

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Chargeback fraud is growing – can AI and Big Data stem the tide?

Monica Eaton, Founder of Chargebacks911
Monica Eaton, Founder of Chargebacks911

According to our research, 60% of all chargeback claims will be fraudulent in 2023. This means not just that merchants have to consider that chargebacks claims are more likely to be fraudulent than legitimate, but that individual merchants and the anti-fraud industry need to lay the groundwork to collect and analyze data that will show them what fraud looks like in real-time.

By Monica Eaton, Founder of Chargebacks911

While many industries are benefiting from so-called ‘big data’ – the automated collection and analysis of very large amounts of information – chargebacks face a problem. The information that is given to merchants concerning their chargeback claims tends to be very limited, being based on response codes from card schemes (‘Reason 30: Services Not Provided or Merchandise Not Received’), meaning that merchants would have to do a great deal of manual work to reconcile the information that the card schemes supply with the information that they have on hand.

While Visa’s Order Insight, Mastercard’s Consumer Clarity, and the use of chargeback alerts have reduced the number of chargebacks, merchants still have very little data on chargeback attempts. This article will look at how merchants can improve the level of data they receive on chargebacks and how they can use this data to create actionable insights on how to improve their handling of chargebacks.

What is big data?

2023’s big tech story is undoubtedly AI – specifically generative AI. Big data refers to the large and complex data sets that are generated by various sources, including social media, internet searches, sensors, and mobile devices. The data is typically so large and complex that it cannot be processed and analyzed using traditional data processing methods.

In recent years, big data has become a crucial tool for businesses and organizations looking to gain insights into customer behavior, improve decision-making, and enhance operational efficiency. To process and analyze big data, companies are increasingly turning to advanced technologies like artificial intelligence (AI) and machine learning.

One example of a company that is using big data to drive innovation is ChatGPT, a large language model trained by OpenAI. ChatGPT uses big data to learn and understand language patterns, enabling it to engage in natural language conversations with users. To train ChatGPT, OpenAI used a large and diverse data set of text, including books, websites, and social media posts. The data set included over 40 gigabytes of text, which was processed using advanced machine-learning algorithms to create a language model with over 175 billion parameters.

By using big data to train ChatGPT, OpenAI was able to create a language model that is more accurate and effective at understanding and generating responses than previous models. This has enabled ChatGPT to be used in a wide range of applications, including customer service chatbots, language translation services, and virtual assistants. Currently, technology very similar to ChatGPT is being used by Bing to replace traditional web searches, with mixed results, but, like self-driving cars, it is a matter of ‘when’, not ‘if’ this technology will become widespread.

AI and fraud

Chargeback fraud is a growing problem for businesses of all sizes. The National Retail Federation estimates that retailers lose $50 billion annually to fraud, with chargeback fraud making up a significant portion of that total. With the significant rise of online shopping, this type of fraud has become even more prevalent, as it is much easier for fraudsters to make purchases using stolen credit card information, forcing victims of fraud to then dispute the charges with their credit card issuer.

Chargeback fraud occurs when a customer disputes a valid charge made on their credit card, claiming that they did not make the purchase or that the merchandise they received was not as described. If the dispute is upheld, the merchant is forced to refund the money to the customer, along with any associated costs, and is typically charged a penalty fee by their payment processor. This not only results in a financial loss for the merchant but can also damage their reputation and lead to increased scrutiny from payment processors.

Where can machine-learning technology help with fraud? To understand this, we have to first understand its limitations. ChatGPT and Large Language Models (LLMs) like it are not Artificial General Intelligence (AGI) – the sci-fi trope of a thinking computer like HAL 9000. Although they can pass the Turing Test, they do so not by thinking about the given information and answering accordingly, but by matching what looks like an appropriate answer from existing text.

This means that while they can produce perfect text by copying existing text rather than ‘thinking’ about the substance of the question, they are prone to producing errors. This is something that isn’t acceptable when it comes to fields like fraud prevention – nonsense answers with a veneer of truth won’t work in the binary world of whether a particular transaction was fraudulent and unfounded accusations of fraud can damage a merchant’s reputation.

What is needed then are AI solutions built specifically for chargebacks. Companies like Chargebacks911 have been working on this for years now, and their solutions are based on big data models that have been built up over that time. Because of their extensive experience working in that field, they are the ideal partner to work with to bring AI up to speed and address the problem of chargebacks.

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