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Banks that embrace predictive analytics and data-driven decision-making are forging a path towards becoming the "extraordinary" banks of the future.
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Predictive Analytics & Data-Driven Decision: Building up the “Extraordinary” Banks of the Future
Predictive Analytics & Data-Driven Decision: Building up the “Extraordinary” Banks of the Future 1300 972 Exist Software Labs

In the fast-evolving landscape of digital banking, staying ahead of the competition and delivering exceptional customer experiences require more than just technology. Banks that embrace predictive analytics and data-driven decision-making are forging a path towards becoming the “extraordinary” banks of the future. As a technology company providing digital banking solutions, Exist Software Labs, Inc. is committed to empowering banks with the tools they need to harness the potential of data and achieve scalable success.

What is Predictive Analytics, and why does it matter?

Predictive analytics leverages historical data, machine learning algorithms, and statistical modeling to forecast future outcomes. For banks, this means harnessing vast amounts of customer data, transaction history, activity patterns, and market trends to make well-informed decisions. By adopting predictive analytics, banks can anticipate customer needs, identify potential risks, and personalize services, paving the way for smarter and more proactive banking experiences.

The Role of Data Warehousing in Predictive Analytics in Banking

Data warehousing acts as the backbone of predictive analytics initiatives. It involves the centralization and integration of data from various sources, enabling banks to access a holistic view of their operations and customer interactions. With a well-structured data warehouse in place, banks can efficiently extract, transform, and analyze data, fueling the predictive modeling process for informed decision-making.

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Embracing the Cloud for Scalability and Flexibility

Cloud technology offers the scalability and flexibility necessary to support the vast amount of data required for predictive analytics. Banks can store and process data real-time, ensuring faster and more accurate predictions. Cloud-based solutions also enable seamless integration with existing banking systems, making it easier for banks to adapt to evolving customer needs and market trends.

From Data Analytics to Data-Driven Decision-Making

While data analytics provides valuable insights, the true value lies in translating these insights into actionable decisions. Banks must foster a data-driven culture, where decisions are based on evidence and data-backed reasoning

How can banks transition from data analytics to data-driven decision-making?

Banks can transition from data analytics to data-driven decision-making by fostering a data-driven culture within their organization. This involves investing in data literacy, promoting a mindset of evidence-based decision-making, and integrating data-driven insights into their strategic planning processes. By embracing data-driven decision-making, banks can unlock the full potential of their data, make informed choices, and achieve greater efficiency and competitiveness in the digital banking landscape.

Conclusion

Predictive analytics and data-driven decision-making are redefining the future of banking. By leveraging the power of data, banks can unlock unprecedented potential — delivering personalized experiences, minimizing risks, and gaining a competitive edge in the market. As a leading digital banking solutions provider, Exist Software Labs, Inc. is committed to empowering banks to participate in the data revolution and helping pave  the way for them to become “extraordinary” banks of the future.

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On Mobile Banking Solutions: How Banks can Dismantle Fraudulence & Identity Theft
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On Mobile Banking Solutions: How Banks can Dismantle Fraudulence & Identity Theft
On Mobile Banking Solutions: How Banks can Dismantle Fraudulence & Identity Theft 1300 972 Exist Software Labs

As the digital banking revolution continues to gain momentum, banks and financial institutions are witnessing unprecedented growth in the use of mobile internet banking solutions. The Philippines, in particular, has seen a surge in the adoption of digital banking, especially among the tech-savvy Gen Z and millennial target market.

The adoption of digital banking among Filipinos is soaring, reaching unprecedented levels. As per the BSP’s study, more than half of Filipino adults (56%) now own a bank account, with 36% of these accounts being digital. Additionally, electronic payments have seen a remarkable surge, with an assumption increase to 50% in 2023, a substantial increase from a mere 1% in 2013. The growing popularity of digital banking can be attributed to the increasing awareness of its advantages, including convenience, security, cost savings, and health and safety reasons, especially during the COVID-19 pandemic.

While this rapid shift towards digital banking offers numerous benefits such as enhanced user experience, convenience, and automation, it also presents a pressing challenge: an alarming increase in fraudulence and identity theft.

A study in 2021 by TransUnion, a global transformation company, said that a 31% increase was observed in digital fraud attempts against enterprises in the Philippines from March 2019 to March 2020, compared to pre-pandemic levels. The sectors with the highest number of suspected digital fraud attempts were telecommunications, logistics, and financial services. 

The article also states that scammers are using fraudulent credit cards to purchase high-end phones and sell them back in the black market. Up to this day, numerous risks exist that banks should be aware of and manage, if not eliminate, by adapting banking solutions and  technologies designed to address these.

As a technology company providing digital banking solutions for banks, Exist Software Labs, Inc. is well aware of the risks associated with this paradigm shift. In this blog, we delve into the complexities of fraud in banking and present actionable strategies that C-levels and CTOs can implement to safeguard their institutions and customers.

1. The Rising Tide of Fraud in Digital Banking

What are the key factors contributing to the surge in fraudulence and identity theft in digital banking?

The rapid transition towards digital banking is attracting the attention of fraudsters seeking to exploit vulnerabilities in the system. Factors such as inadequate security measures, weak user authentication, and the growing sophistication of cybercriminals have contributed to this surge in fraudulence.

2. The Imperative of Enhanced Security Measures

How can banks fortify their digital banking platforms against fraud and identity theft?

To dismantle fraudulence and protect customers’ identities, banks must prioritize security at every step. Implementing multi-factor authentication, biometric identification, and robust encryption protocols are some of the measures banks can adopt. Exist Software Labs’ digital banking solutions make use of cutting-edge security features to provide a shield against potential threats.

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3. Leveraging AI and Machine Learning

How can AI and machine learning technologies help combat fraud?

AI-powered fraud detection systems can analyze vast amounts of data in real time, identifying suspicious patterns and transactions. By continually learning from new data, these systems can adapt to evolving fraud tactics. Exist Software Labs, Inc., integrates AI and machine learning algorithms into its digital banking solutions, providing an extra layer of protection for banks and their customers.

4. Educating Customers and Raising Awareness

How can banks empower their customers to protect themselves from fraud?

Empowering customers through education is vital in the fight against fraudulence and identity theft. Banks should regularly communicate security best practices, raise awareness about common scams, and offer tips on safeguarding personal information. By fostering a sense of vigilance among their customer base, banks can create a collective defense against fraudsters.

5. Collaboration within the Industry

To combat fraud effectively, the entire banking industry must collaborate. Banks can share threat intelligence and best practices with each other, creating a united front against fraudsters. Additionally, forming partnerships with industry-leading digital banking solutions providers like Exist Software Labs, Inc., enables banks to access the latest security advancements and stay one step ahead of potential threats.

As digital banking continues to redefine the financial landscape, the battle against fraudulence and identity theft is only intensifying. C-levels and CTOs must recognize the urgency of this issue and take proactive steps to safeguard their institutions and customers.

Exist Software Labs, Inc., a leading digital banking solutions provider, understands the challenges faced by banks in this rapidly evolving landscape. By prioritizing security, leveraging cutting-edge technologies, and fostering a culture of awareness, banks can dismantle fraudulence and protect their customers, allowing them to embrace the full potential of mobile banking with confidence.

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Befriending Your Data in 2021, Java, Java Philippines
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Befriending Your eye-opening Data in 2021
Befriending Your eye-opening Data in 2021 768 487 Exist Software Labs

It’s the new year and everybody is still living in the wake of the COVID-19 pandemic. We all need a friend in times of trouble and this is no different in the case of business organizations.

This year, 2021, the friend that your company needs more than ever, especially in these trying times, is data.

Given the disruption that this virus caused in the preceding year, enterprises need to start (if they haven’t already) befriending their own internal data, and perhaps external data as well if they are to at least stay viable and at most grow.

The following are some insights from respected data management leaders on how to make friends with your data this year:

  • Eran Vanounou, CEO at Varada

    “Data warehouses are not going to disappear. Data warehouses will continue to be an important legacy technology that organizations will use for mission-critical business applications well into the future.

    With the transition to the cloud, data warehouses got a fresh new look and offer some modern attractive capabilities including self-service and serverless.

    With the rise of the cloud, data lakes are the new kid on the block. Data lakes are becoming a commodity, a legacy technology in their own right. Their rapid emergence from the innovation stage means two things going forward.

    First, organizations will demand simpler, easier to manage, and more cost-effective means of extracting usable business intelligence from their data lakes, using as many data sources as possible.

    Second, those same organizations will want the above benefit to be delivered via tools that do not lock them into proprietary data management platforms.

    In short, 2021 will begin to see the rapid introduction and evolution of tools that allow users to keep their data lakes in one place and under their control while driving performance up and cost down.”

  • Paige Roberts, Open-Source Relations Manager at Vertica

    “Distributed analytical databases and affordable scalable storage are merging into a single new thing called either a unified analytics warehouse or a data lake house depending on who you’re talking to.

    Data lake vendors are scrambling to add ACID capabilities, improve SQL performance, add governance, resource management, security, lineage, and all the things that data warehouse vendors have been perfecting for the last three or four decades.

    During the ten years, while data lake software has been coalescing, analytical databases have seen their benefits and added them to their existing stacks: unlimited scale, support for widely varied data types, fast ingestion of streaming data, schema-on-read, and machine learning capabilities.

    Just like a lot of things used to claim to be cloudy before they really were, some vendors will claim to be a unified analytics warehouse when they’ve just jammed the two architectures together into a complicated mess, but everyone is racing to make it happen for real.

    I think the data warehouse vendors have an unbeatable head start because building a solid, dependable analytical database like Vertica can take ten years or more alone.

    The data lake vendors have only been around about ten years, and are scrambling to play catch-up.”

  • Raj Verma, CEO at SingleStore

    “One single SQL query for all data workloads

    The way forward is based not only on automation but also on how quickly and widely you can make your analytics accessible and shareable.

    Analytics gives you a clear direction of what your next steps should be to keep customers and employees happy, and even save lives. Managing your data is no longer a luxury, but a necessity–and determines how successful you or your company will be.

    If you can remove the complexity or cost of managing data, you’ll be very effective.

    Ultimately, the winner of the space will take the complexity and cost out of data management, and workloads will be unified so you can write one single SQL query to manage and access all workloads across multiple data residencies.”

  • Prat Moghe, CEO at Cazena

    “Expect more enterprises to declare the battle between data lakes and data warehouses over in 2021 – and focus on driving outcomes and modernizing.

    Data warehouses can continue to support reporting and business intelligence, while modern cloud data lakes support all analytics, AI and ML enablement far more flexibly, scalably, and inexpensively than ever – so enterprises can go transform quickly.

    Cloud migrations and related cloud data lake implementations will get demonstrably faster and easier as DIY approaches are replaced by turnkey SaaS platforms.

    Such solutions will slash production cloud data lake deployment times from months to minutes while controlling costs and providing the continuous operations, security and compliance, AI and ML enablement, and self-service access required for modern analytics initiatives.

    That means that migrations that used to take 9-12+ months are complete in a fraction of the time.”

  • Adi Paz, CEO at GigaSpaces Technologies

    “Co-locating analytics and operational data results in faster data processing to accelerate actionable insights and response times for time-sensitive applications such as dynamic pricing, hyper-personalized recommendations, real-time fraud and risk analysis, business process optimization, predictive maintenance, and more.

    To successfully deploy analytics and ML in production, a more efficient Data Architecture will be deployed, combining OLTP (CRM, ERP, billing, etc.) with OLAP (data lake, data warehouse, BI, etc.) systems with the ability to build the feature vector more quickly, and with more data for accurate, timely results.”

To summarize the various points made by these industry pundits:

1

SQL-driven data warehouses are here to stay and will continue to be the data analytics platform of choice for enterprises in the current year.

2

Data management platforms that integrate well with existing data lakes will dominate as opposed to platforms that focus on one or the other.

3

Data management platforms that have built-in AI/ML functionalities will dominate as well, as this eliminates the cost and complexity of separate AI/ML analytics platforms.

4

Data management platforms that are cloud-ready will also have an edge over those that are not.

Is there a data management platform that possesses all these qualities and has a proven track record in Fortune 500 companies?

Yes, there is. It’s called Greenplum. Read about it here.

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