Understanding OCR

OCR is a technology used to convert scanned documents or images containing text into computer-readable text, allowing automated data extraction and analysis.

Real-life Applications of OCR in Automation Testing

Extracting Data: Extract crucial information like invoice numbers from invoices, receipts, or forms. By using this, we can perform validations, ensuring that software correctly processes and stores such information.

Test Data Generation: Reads test data from legacy systems or documents and creates test scenarios and test cases, reducing manual effort in data preparation.

Example 1: Extract product details, prices, and customer information from invoices and purchase orders. This is used to perform end-to-end testing, ensuring accurate order processing and improving customer experience.

Example 2: Digitize prescriptions and medical reports which are used in automated testing of EHR systems, guaranteeing the correct storage and recovery of patient information, medications, and treatment histories.

Introduction to NLP

NLP is a branch of artificial intelligence that helps computers understand, interpret, and generate human language. Its role is to bridge the gap between human communication and machine understanding, allowing software to process, analyze, and respond to text and speech data in a way that resembles human language comprehension.

Real-Time Examples of NLP in Automation Testing

Log Analysis: Identifies patterns and errors in log data, automates the detection of exceptions, and reduces the need for physical log inspection.

Test Case Generation: Converts natural language requirements into executable test cases. By translating textual descriptions of desired functionalities, NLP streamlines test case creation, ensuring that test cases accurately reflect intended behavior and reducing the time required for test design and scripting.

Chatbot Testing: By simulating user conversations with natural language, NLP ensures the chatbot’s understanding and ability to provide appropriate responses, improving overall functionality and user experience.

Accessibility Testing: Assesses the clarity and correctness of textual content for screen readers and visually impaired users.

Localization Testing: Automatically compares source and target language content to ensure that localized versions of software or websites accurately reflect the original text and cultural requirements for various global audiences.

Integration of OCR and NLP

Combining OCR and NLP in automation testing allows for advanced capabilities, such as extracting and comprehending text from images or documents, enabling sophisticated data validation and test case generation.

Extracting Text from Images: OCR can extract text from images, making content machine-readable. NLP can then analyze the extracted text, allowing automation scripts to validate the information in image-based UI testing.

Sentiment Analysis on User Reviews: NLP can perform sentiment analysis on user reviews, categorizing opinions as positive, negative, or neutral. Combined with OCR, you can extract textual reviews from images or unstructured data sources, enabling automation to assess user sentiment without manual data entry.

Benefits of Using OCR and NLP in Automation Testing

The integration of OCR and NLP minimizes manual effort in data entry and test case generation, allowing testing teams to focus on higher-level tasks. Additionally, these technologies excel at handling complex scenarios, such as analyzing vast amounts of textual and visual data, enhancing test coverage, and overall testing effectiveness.

Conclusion

In conclusion, the synergy of OCR and NLP in automation testing promises a transformative leap in efficiency, accuracy, and coverage, ushering in a new era of software quality assurance where intricate testing challenges can be met with ease, precision, and speed.

The post The Role of OCR and NLP in Automation Testing appeared first on Indium.

• Volatile market conditions,
• Uncertainty around interest rates and inflation,
• Complex regulatory requirements, and
• Ambiguous economic indicators,

Together it’s a VUCA world, and the industry faces numerous hurdles.

Not Without Pitfalls

With vast amounts of data to process and analyze, the quality and reliability of financial data can be compromised, leading to errors, inconsistencies, and fraud. To keep up, financial services organizations must embrace data analytics, AI, and other emerging technologies to optimize operations, reduce costs, manage risks, and deliver value to their customers. Those who fail to do so risk falling behind in the VUCA world of today.

According to Gartner’s “IT Score Benchmarks for Data & Analytics in Banking, Finance, and Insurance,” BFSI organizations are investing heavily in data and analytics capabilities to improve their decision-making, customer engagement, and risk management.

1. Maturity levels in data and analytics indicate how advanced an organization is in implementing and utilizing data-driven strategies and technologies, from early stages to advanced adoption.
2. Importance levels reflect the significance of data and analytics activities in driving business value, decision-making, and achieving strategic goals.

However, the report highlights the top 3 biggest gaps between importance and maturity levels, which will be shared exclusively with you. By understanding these levels, organizations can prioritize efforts to improve data quality, implement advanced analytics techniques, and leverage data for informed decision-making, ultimately enhancing their overall data and analytics capabilities.

Challenges to Adopting Data Analytics Transformation in the Banking Industry

Above all, a well-crafted strategy is essential. Without a clear plan of action, investing in data analytics tools and technologies is a waste of time and resources. Here are the “Top 3 Insights from Gartner’s 2023 IT Score Benchmarks for BFSI”

Source: Gartner

Every data analytics effort should be built on a well-defined plan. Without one, you may buy all the technology globally and still spin your wheels. Any effective strategy begins with determining the main goals and objectives you want to accomplish. Not only will this offer you a clear way forward, but it will also make it simpler to interact with key decision-makers. Every company-wide project needs executive buy-in, so adequately explaining what your organization stands to gain through implementing banking analytics may assist in securing a “yes” from those in positions of authority.

Defining which metrics and key performance indicators to measure is difficult if important goals and objectives are not identified. Without a clear vision of what you’re striving for or how to measure success, it becomes more difficult for employees at all levels of your business to support banking analytics projects, which can interfere with organizational change management (OCM) and user adoption efforts. It also prevents you from implementing advanced data analysis in banking as effectively as you might or should: You may ask incorrect questions, preventing algorithms from providing useful insights.

Once you’ve established a solid plan, the next steps are to develop excellent data governance and execute the appropriate technology. Data governance assures that all large data gathered in banking is ethical, compliant, and responsible. It establishes essential requirements for where data is stored, how it is accessed, and how it is utilized — all of which guarantee that your employees are supported and that your data procedures are long-term. This is a target that most businesses in the BFSI sector have not met. But where do we stand on a scale of 1 to 5?

One important aspect missing from our list is setting a clear aspiration. However, we believe every institution should aim to establish analytics as a core business discipline used by decision-makers throughout the organization.

Analytics should be like a reflex, much like the human nervous system, with every part of the bank knowing how to react to specific stimuli. At the same time, different banks may have varying paces in building and training their analytics systems, some nerve paths will already be established, while others need to be developed and taught to react accordingly.

Then, old legacy systems that keep data segregated across multiple lines of business — even from one team member to the next — must be replaced. These divisions need greater work from both consumers and customer-facing team members, slowing operations and severely impacting the customer experience. As a result, banks and other financial institutions must invest in advanced analytics solutions that enable them to address difficulties and provide financial advice in a timely manner.

7 Tips for Success

Developing a successful banking analytics strategy requires careful planning and execution. Here are some tips to help set you on the right path:

1. Start Small & Scale Gradually: Instead of trying to take on too much too soon, focus on achieving small wins that can fund future projects while delivering the most significant ROI.

2. Adopt an Iterative Learning Approach: Learn from the experience and adopt an iterative approach to treat each project as an opportunity to improve and learn.

3. Build a Comprehensive Data Ecosystem: Utilize internal & external data sources to build a more comprehensive data ecosystem, providing valuable context and enhancing insights from internal data.

Source: Gartner

4. Right Questions: Determine what information you want to put forward; the right questions that will help you obtain the most meaningful data before committing resources.

5. Choose User-Friendly Solutions: Look for banking analytics solutions that feature intuitive and visually appealing visualizations and dashboards that make data-driven insights easily accessible and understandable.

6. Obtain Executive Buy-In: Executive buy-in is crucial to ensure that employees at all levels of the business are on board with new systems and strategies.

7. Automate Where Possible: Automating low-level service requests saves valuable time and allows employees to focus on high-level requests that drive greater value.

Remember to align your strategy with performance metrics, KPIs, and governance, and assemble a winning team with both data science expertise and industry experience. By following these tips, you can develop a successful banking analytics strategy that drives meaningful insights and improves business outcomes.

Energy Shots!

Digital transformation is a never-ending journey for BFSI, as they need to constantly evolve to meet changing customer demands, comply with new regulations, and stay ahead of their competitors. But upgrading legacy systems is not a quick fix and can be complex. Banks must prioritize the most critical areas for modernization and ensure that the transformation is comprehensive, swift, and customer-centric.

To successfully upgrade their core banking systems, banks should:

1. Create a clear roadmap that aligns with their vision,
2. Identify key metrics for measuring success, and
3. Expand their team with domain experts, data specialists, and tech professionals.

They should also prioritize cleaning up their data and ensuring it’s of high quality, conduct training for staff, and document all functional and technical knowledge around the core banking system.

Digital transformation requires careful planning and execution, and banks must be prepared to face new risks and challenges. By following these steps, banks can future-proof their operations, improve customer experiences, and stay competitive in the dynamic financial landscape.

EXCLUSIVE UPDATE – eBook coming soon!

The post Pivot or Persevere? Insights from Gartner’s IT Score Benchmarks for Data & Analytics in BFSI appeared first on Indium.

Even though the financial services industry has long been affected by technological advancements and disruption, many FinTech technologies have been hesitant to penetrate the lucrative wealth management market.

Due to the numerous stakeholders in the wealth management sector, we are aware that the market is considerably larger than merely advisers.

So, let’s take a broader look and explore some of the industry’s current trends that you should be aware of.

1. Re-Platforming

One of the largest developments in the field is Re-Platforming, which is also known as “upgrading.” Re-platforming is merely an upgrade that entails swapping out your platform’s outdated IT technologies for more recent ones.

Given how long it takes to implement new systems, this upgrade is crucial, and making these updates is important since it will affect your customers and how you interact with your provider.

Re-platforming or upgrading may be a laborious process that requires years to complete before you reach your desired level, but the results are rewarding.

2. Limiting Compliance Cost

The regulatory compliance reporting process may be simplified to a large extent by technology. As a consequence of continuous changes, businesses and professionals are increasingly using affordable solutions that streamline operational processes.

Partnerships with solution providers are anticipated to become essential for firms to quickly respond to changes in the regulatory landscape and to confidently fulfill regulatory requirements.

3. Regulatory Technology

Regulations may and usually do change. To ensure that it is always compliant, your business has to have a strong compliance system in place.

RegTech tools may assist your wealth management business cut costs on fines, manpower, and the cost of enacting new laws. In order to help your business with regulatory compliance, choose a RegTech solution.

Regulation technology is a significant development that wealth management professionals need to be aware of. Companies spend a lot of money each year dealing with compliance concerns, even if it’s simply making sure that you are being compliant.

RegTech reduces compliance-related problems while improving the effectiveness and efficiency of the whole compliance process.

It might be interesting to read about Challenges in the Wealth Management Industry and How can Tech Reshape it

4. Agile Distribution

Professionals in wealth management have turned their attention from goods to customer service. Companies are increasingly making substantial use of automation and insight-based solutions.

With the use of this technology, businesses may respond to customer expectations for individualized services and market developments.

The main goal of agile distribution is to enable next-generation capabilities by changing a company’s service delivery paradigm to one that is based on agile principles.

From a regulatory and operational standpoint, technologies like cloud computing, big data, APIs, and artificial intelligence allow businesses to adapt without having to implement whole new systems that are unable to match the shifting demands.

In other words, agile distribution refers to contemporary technology that is quick to respond to changes in operational, legal, and customer demand without requiring the implementation of new technology or specialized development effort.

This eventually makes it simpler to service clients and increase efficiency, enabling businesses to respond to changing client requests more quickly.

5. Use of Virtual Tools

Numerous changes in wealth management are undoubtedly being driven by technology advancements in the sector.

Despite the industry’s past reluctance to accept new technology, developing tools provide fantastic opportunity for businesses to increase their bottom line.

By assisting customers with frequent, automated reports of their assets, virtual tools can free up advisors’ time so they can concentrate on finding new clients and giving advice. Additionally, automating numerous customer contacts eases financial strains.

By facilitating data-driven judgments, virtual tools might also encourage investors to modify their behavior. And by proposing actions for customers, tools like next-best-action engines may assist advisers in streamlining their roles and duties.

6. Internet of Things

Despite the widespread use of Internet of Things (IoT) technology, the wealth management industry has been slower than others to do so. The time has arrived to start making future plans and offering your clients better product choices.

For asset managers, IoT devices open up new data sources, but capturing real-time spending and saving data does more than just allow them to give more tailored solutions.

Furthermore, it assists clients in discovering areas of their lifestyle that they might not have been consciously aware of.

IoT technology is typically employed for practical tasks like monitoring a client’s assets, but it may also be applied to improve operational efficiency, satisfy customers, and make products more affordable.

7. Augmented and Virtual Reality 

Tools like augmented reality and virtual reality are excellent for helping wealth managers connect with the younger customers who are beginning to take over the industry.

These innovative technologies not only assist in luring in younger consumers, but they also aid in educating them about the advantages of wealth management in a way that is more engaging and simpler to comprehend.

Long-term success will depend on how well a wealth management company can explain the value of their service to younger clients who may not be familiar with the services they offer.

AR and VR will be instrumental in helping forward-thinking organizations communicate with and educate their potential clients, which is something that wealth management firms must be creative about.

The most intriguing and promising technological advancements now in development are augmented reality (AR) and virtual reality (VR).

Despite the fact that it can appear like AR and VR have no role in wealth management, that is just untrue.

8. Machine Learning to Correct Bias Decisions

One of the pillars of wealth management is decision making. Even the most seasoned wealth managers are prone to making biased decisions.

This is unarguably why a wide range of sectors and companies have adopted the use of machine learning and found it to be profound.

The accuracy of the predictions made by machine learning techniques will increase with the amount of data you provide.

Successful wealth management companies can recognize when emotion or intuition is driving a trade and make rational change as a consequence.

The fact that the bulk of high-net-worth individuals are older and accustomed to conducting business in a particular way is perhaps the main reason why the wealth management sector has lagged behind the financial services sector overall.

On the other hand, you may be confident that you’ve done a fantastic job based on worldwide benchmarks thanks to these technological advancements.

How can Indium help

We at Indium, can –

• Automate wealth management workflow thus integrating front office, middle office, sales, and back-office functions.
• Our award winning Tex.ai can be used to extract insights from various sources, thereby helping financial institutions create customized products and services and focused promotions to improve conversions and enhance overall customer experience.
• By leveraging our cloud solutions financial institutions can modernize their infrastructure, meet rapidly changing customer behaviours and expectations, and drive business growth.

The post Top Technology Trends Driving the Wealth Management Industry  appeared first on Indium.