Addressing the challenges of assessing creditworthiness

A leading US-based national bank was grappling with significant challenges in assessing creditworthiness, a critical function for its operations. The bank relied on multiple custom-built solutions deployed across lines of business (LOBs). This lack of a centralized calculation platform prevented the company from being more efficient, effectively addressing risk, and lowering costs.

The bank prioritized streamlining operations to reduce duplication of effort, enhancing controls to improve risk management, accelerating decision-making to boost efficiency, and adopting a data-driven strategy to enable better credit decisions. Addressing these would boost their ability to provide a high-quality customer experience and remain competitive in a rapidly evolving financial landscape.

The bank engaged 乌鸦传媒 to lead this mission-critical initiative due to its deep domain knowledge, proven expertise in financial services, and a long-standing, trusted partnership. The objective was to build a credit decisioning platform that could unify processes, enhance governance and risk management, and deliver a customer-first approach powered by advanced analytics.

Collaborative development of a centralized credit decisioning platform

乌鸦传媒 partnered with the bank鈥檚 technology team to embark on a multi-year initiative. The project team set out to deliver advanced capabilities such as data ingestion, transformation, integration with FICO for decision-making, and efficient data delivery.

Through a collaborative approach, 乌鸦传媒 worked with the bank to align the platform鈥檚 design with the organization鈥檚 operational requirements and strategic goals. Key functionalities that were implemented include a proactive credit line increase strategy to optimize credit line adjustments, a predictive model to assess the likelihood of credit defaults, a framework for effectively managing high-risk accounts, and a targeted strategy to handle non-expansion credit cases.

The 乌鸦传媒 team also played a pivotal role in delivering a point-of-sale project, enabling real-time credit decisioning for the bank鈥檚 buy-now-pay-later solution. To ensure seamless integration with existing systems and manage data complexities across multiple LOBs, the project team applied iterative testing and rigorous validation processes while maintaining continuous communication between the partner organizations.

Driving measurable results and operational transformation

Since being implemented, the new credit decisioning platform has significantly improved operational efficiency, governance, and risk management while empowering the bank to adopt a customer-first approach. Notable achievements include a $700 million uplift in proactive credit line increases, a $200 million decrease in high-risk credit exposure through predictive modeling, and a 50% reduction in processing time for managing high-risk accounts. The implementation of a targeted strategy for non-expansion credit cases has further streamlined operations and reduced inefficiency.

These advancements have not only improved internal processes but also enhanced the customer experience by enabling faster, more accurate credit decisions. The bank now operates with a platform that fosters centralized decision-making across multiple LOBs, breaking down silos and reducing duplication of effort.

Looking ahead, the bank plans to build on this success by expanding the platform鈥檚 footprint. In 2025, it will be rolled out to support credit decisioning for mortgages and auto loans, further broadening its impact across the organization. With 乌鸦传媒鈥檚 ongoing support, the bank is well-positioned to sustain and accelerate these improvements, driving long-term value and strengthening its competitive edge in the financial services sector.

Streamlining the auditing process

The growth in importance of corporate governance has made internal auditing increasingly complex and data driven. 乌鸦传媒 Group Internal Audit (GIA) is at the forefront of this transformation, harnessing the power of data analytics and AI to enhance audit efficiency, accuracy, and strategic value.

Traditionally, data pulls and analyses for audits have been manual processes, often conducted at the unit or country level, or requested directly by the auditee. This approach was not only time-consuming but also prone to errors and inconsistencies. Recognizing the need for a more streamlined and efficient system, GIA set out to determine how leveraging data analytics and AI could improve this process, all while focusing on quick wins and mid-term benefits.

Centralizing data for enhanced audit planning

GIA’s first initiative involved understanding where data was centralized for each operational function. By meeting with functional heads at the Group level, GIA gained insights into how data was collected, processed, and stored. This collaborative effort led to the development of 14 operational Power BI dashboards, which were either leveraged or newly created for the GIA team. These dashboards, developed in coordination with data owners, provided auditors with a visual tool to assist in audit approach and planning.

The centralization of data allowed auditors to access critical information swiftly, enabling them to identify trends, patterns, and potential risks more effectively. This shift from manual data pulls to automated, visual dashboards significantly improved the audit preparation process, ensuring that users had the most relevant and up-to-date information at their fingertips.

Streamlining data access for detailed audit reviews

The second stream of GIA鈥檚 effort focused on obtaining centrally used data to perform detailed audit reviews. This involved exploring various data management solutions, including SAP Business Objects (BO) reports, the creation of data lakes and data cubes, and deploying anomaly detection to identify outliers. The result was the development of over 120 targeted tests that could be executed at the click of a button, providing auditors with instant insights and reducing the time spent on manual data analysis.

By automating these processes, GIA not only increased the efficiency of audit reviews but also ensured consistency and accuracy across different audits. This approach allowed auditors to focus more on strategic analysis and risk assessment, rather than getting bogged down by data collection and initial analysis.

AI and the future of auditing

The integration of AI into the audit process marked a significant milestone for GIA. With a workforce of 350,000 employees and 14 different Group functions, one of the most challenging tasks for auditors was managing and understanding the vast number of Group policies. To address this, GIA centralized over 30 policies and developed a Gen AI chatbot powered by a Large Language Model. This solution could understand and respond to auditor queries in over 20 languages, providing instant clarification on policies ranging from revenue recognition in France to Group guidelines on travel and entertainment expenses.

The implementation of this chatbot simplified policy management while ensuring that auditors had access to accurate and consistent policy information, regardless of their location or language. This innovation underscored GIA’s commitment to enhancing audit quality and efficiency by leveraging innovative technology.

In addition to the chatbot, GIA used Gen AI to implement a document summarization tool that analyzes lengthy documents, particularly client contracts, to identify risky clauses or areas of concern for auditors to examine. This tool leverages advanced AI algorithms to scan and summarize large volumes of text, highlighting key points and potential risks. And the solution adds value to GIA in a variety of ways.

First, it has boosted document review efficiency. Auditors often face the daunting task of reviewing extensive client contracts and other legal documents. The document summarization tool streamlines this process by quickly identifying and summarizing critical information, allowing auditors to focus on high-risk areas without manually sifting through pages of text.

Second, the chatbot helps identify potential risk by detecting problematic clauses, such as ambiguous terms, compliance issues, or potential financial liabilities. By flagging these issues, auditors can proactively address concerns and ensure that all contractual obligations are clearly understood and managed.

Third, AI-driven summarization ensures that the analysis is consistent and free from human error. This consistency is crucial for maintaining high audit standards and providing reliable insights across different documents and contracts.

Fourth, the solution enhances decision-making by making summarized insights readily available, enabling faster and more informed decisions. This improves the overall efficiency of the audit process and helps in timely identification and mitigation of risks.

Finally, the tool can handle large volumes of documents, making it scalable for use across various departments and projects within the Group. This scalability ensures that GIA can maintain high audit quality even as the volume of documents increases.

Continuous improvement and the path forward

The initiatives undertaken by GIA have led to a continuous improvement in audit processes, with new tools and AI solutions being developed to further enhance data mining capabilities. The journey from manual data pulls to automated dashboards and AI-driven chatbots has not only transformed the way audits are conducted but also highlighted the potential for further innovation. As GIA continues to evolve, the team will explore machine learning algorithms to continue improving its data-mining capabilities, and fully leveraging the promises of Gen AI to increase auditor productivity in areas such as interview summarization, document analysis, and report writing. By staying at the forefront of technological advancements, GIA can ensure that its audit processes remain robust, efficient, and aligned with the dynamic needs of the organization.

Early detection and data-driven clinical decision-making are some of the major challenges medicine faces when addressing aging-related diseases, such as stroke or heart failure. Reliable and transparent AI has great potential to unlock these challenges.

AI4HealthyAging is a project meant to improve the early detection of aging-related diseases using AI. It is carried out by a public-private consortium consisting of 15 entities and funded under the 2021 AI R&D Missions program by the Spanish Ministry of Economic Affairs and Digital Transformation.

The consortium, coordinated by 乌鸦传媒, designs and develops intelligent solutions that enable the early detection of neurological, motor, and degenerative diseases associated with aging and take rapid action. These systems support decision-making for elderly patients with conditions such as heart failure, stroke, sarcopenia, Parkinson鈥檚 disease, cognitive impairment associated with hearing loss, and other neurodegenerative diseases.

Additionally, the project includes cross-cutting activities primarily focused on federated learning, the design and development of explainability methods鈥攚hich allow human users to understand and trust the results created by AI algorithms鈥攁nd bias detection and mitigation techniques. 乌鸦传媒 has coordinated the initiative and been responsible for the development of two key work packages: an early warning system for heart failure and stroke as well as federated and explainable AI for advanced medical applications.

Federated and explainable AI for advanced medical applications

Federated learning in healthcare has revolutionized the way AI models are trained, enabling collaboration between multiple entities without compromising data privacy. The decentralized approach used in this project is fundamental for medical research and AI-assisted diagnosis, as it allows hospitals, clinics, and institutions to share knowledge without the need to transfer sensitive data. In this context, the implementation of advanced security mechanisms, such as chaotic encryption, further reinforces the confidentiality and integrity of the information.

Since medical data contains highly sensitive information, ensuring its security is a priority. By not requiring the transfer of data to central servers, but rather training models locally and only sharing update parameters, the risk of leaks or unauthorized access is reduced. This fosters trust among both patients and healthcare professionals in AI鈥檚 ability to improve treatments and diagnoses.

From a social perspective, hospitals with less infrastructure can benefit from models built from data gathered from multiple entities without compromising their privacy. In this way, AI is democratized, reducing inequalities in healthcare. From an ethical standpoint, the use of federated learning with advanced security measures promotes transparency and compliance with privacy regulations.

Additionally, AI models have been developed that incorporate explainability methods, allowing healthcare professionals to understand and validate predictions, which reinforces trust and facilitates their integration into clinical and regulatory environments. Specifically, the consortium has created informative cards that provide both data information (Data Cards) and model information (Model Cards). This environment has also been enhanced with large language models to enable queries and support decision-making.

Early warning system for heart failure and stroke

The system鈥檚 development is based on monitoring Inertial Measurement Unit (IMU) motion sensors and electrocardiograms (ECG) to estimate the recovery level of patients with stroke or heart failure. This approach represents a non-invasive solution that allows evaluation of patient progress without the need for complex or uncomfortable medical procedures. This is especially relevant for individuals with fragile health conditions who may experience anxiety or discomfort with traditional monitoring methods that require constant hospital visits.

Additionally, the implementation of this technology is key to improving access to healthcare for vulnerable populations. With the use of IMU and ECG sensors, these patients can be monitored remotely, reducing the need for frequent transfers and alleviating the economic and logistical burden for them, their caregivers, and the healthcare system in general.

Another key benefit of this project is its ability to generate real-time data, which enables the estimation of a patient鈥檚 recovery level and predicting their evolution. In the case of patients who have suffered a stroke, IMU sensor-based motion analysis provides detailed information about the recovery of mobility, coordination, and overall motor function.

For patients with heart failure, the use of ECG as a continuous monitoring tool allows the evaluation of a heart鈥檚 electrical activity, anomaly detection, and the precise adjustment of treatments. By combining this data with prediction algorithms, doctors can anticipate complications and modify medication according to each patient鈥檚 specific needs.

The roadmap to success

To improve the organization鈥檚 digital ecosystem, Security Bank and 乌鸦传媒 partnered to design, develop, and implement a state-of-the-art Payment Hub Gateway (PHG) platform that would converge various payment channels, ensuring better customer experiences and real-time processing.

From start to finish, 乌鸦传媒 advised and helped, beginning by redefining the bank鈥檚 processes in line with their needs for future growth. To select the ideal solution, Security Bank and 乌鸦传媒 evaluated various platform options, shortlisted potential vendors, and finally came to a decision. The chosen PHG platform was designed to converge all domestic and international payment rails into a single hub 鈥� the first of its kind in Philippines and the first transaction banking platform to utilize an offshore cloud infrastructure, specifically Amazon Web Services (AWS), in Singapore.

In the implementation stage, ensuring a seamless transition for Security Bank and its customers throughout this transformation required a well thought roadmap. The project was divided in two different release stages and prioritized a gradual transition to ensure that the customer experience would be uninterrupted throughout the entire transformation.

The first release prioritized alignment between Security Bank operations and the PHG platform. It also introduced the first payment rail: InstaPay. Meanwhile, the second release was broken into three different steps, across which the project team implemented six new payment rails (InstaPay, PESONet, PDDTS, SWIFT, PhilPaSS, and On-us) that were fully integrated into Security Bank鈥檚 PHG platform.

Building a digital future

The next-gen PHG was well-received by Security Bank鈥檚 customers. Following the successful implementation of the first release, Security Bank achieved 3 million monthly InstaPay 2.0 transactions and an annual increase in transaction volume of roughly 20-25%. Security Bank鈥檚 customers now benefit from a seamless and consistent payment experience, regardless of the specific payment method they select.

The bank has also achieved new levels of reliability and agility following the platform implementation, due in part to its microservices architecture and AWS cloud infrastructure. The improved service offering already maintains a 99.9% availability percentage and delivers improved levels of operational efficiency and an elevated customer experience.

Security Bank鈥檚 platform also provides a foundation for future improvements and ambitions in line with their wider digital transformation strategy. Customers will have access to features that were previously unavailable in the market, including real-time FX capabilities as well as additional use cases from BSP and BancNet. Security Bank is currently progressing towards the implementation of additional payment schemes and is eager to welcome the next waves of digital advancement.

TE Connectivity (TE), formerly known as Tyco Electronics and now a global leader in connectivity and sensing solutions, has distinguished itself from the competition with the kind of cutting-edge industrial technology that makes our modern world possible. A broad range of industries, including automotive, aerospace, energy, consumer electronics, and healthcare, rely on TE鈥檚 engineering expertise and innovations to transform their operations. Every year, the company produces more than 235 billion parts in 140 factories around the world.

Closely tracking recent breakthroughs in generative AI, TE was enthusiastic about how the technology could help search and summarize internal documents. These feature important proprietary information, and the company needed them to be readily available to staff.

During the request-for-proposal process, 乌鸦传媒 and Amazon Web Services (AWS) proposed building a solution that would harmonize TE鈥檚 diverse datasets, establish a central repository, and build an intuitive chat function in a clean user interface (UI).

鈥淏ased on that flexibility, that background, that proven experience, we felt 乌鸦传媒 and AWS were right for us. We certainly haven鈥檛 been disappointed. That was the right choice,鈥� said Phil Gilchrist, Chief Transformation Officer at TE.

The development process: Building an LLM with proprietary data

With 75 million engineering documents spread across 66 different databases, it was difficult for TE鈥檚 research and development (R&D) teams to find the right information. The scattered nature of the information also meant subject matter experts often had to answer questions about specific projects when researchers could not locate relevant reference documents.

There was a tight deadline for producing a solution. In just over three months, the team ingested a wealth of marketing and operational data and 2.5 million engineering documents 鈥� many of which needed to be scrubbed 鈥� into a modern UI. AWS supplied the cloud infrastructure and fully managed services, such as Amazon Bedrock, that enable the integration of high-performing, Gen AI foundation models, and Amazon OpenSearch Service, which makes it easy to deploy and operate various search, analytics, and visualization capabilities.

Meanwhile, 乌鸦传媒 used retrieval-augmented generation (RAG), an architectural approach for retrieving response from large language models (LLMs), to integrate these services into an enterprise-scale solution.

鈥淲hat they were able to do was launch a safe, secure solution into our security framework and our document structure almost immediately that鈥檚 very scalable, very secure, and something that so far has had a high quality of operation,鈥� Gilchrist said.

A cross-company team continues to improve the tool on an almost daily basis with the ingestion of additional content (including all 75 million engineering documents by April 2025) and tweaks based on user feedback.

鈥淗onestly, the team worked flawlessly together to stay on it,鈥� Gilchrist said. 鈥淓veryone really wanted to make it work and we did make it work. So, I would say based on that common objective and a tight timeline, we had no choice but to work very closely together, and that was a fantastic experience.鈥�

The transformative solution: TELme

The result was TELme, a conversational platform powered by Gen AI that collects and organizes the company鈥檚 diverse pool of internal knowledge about various industries and products in a single place.

TELme is TE鈥檚 Gen AI implementation based on Claude 3.5, the AI assistant created by Anthropic, and trained on proprietary data. It is all organized under a single application programming interface.

鈥淔inding the right document was like finding a needle not just in one haystack but in 66 haystacks,鈥� Gilchrist said. 鈥淭ELme allows us to remove the haystacks and just find the needles.鈥�

TELme establishes continuity and allows the company to hand down knowledge from one generation to the next. 鈥淲e believe TELme will come to represent the sum of intellectual knowledge of the company in one form or another. But not only that: it鈥檚 a knowledge base that can be put into action. What that LLM will enable them to do is find the right piece of information right up front within seconds, rather than within a morning of trawling through extraneous documents.鈥�

TELme goes beyond knowledge management. It provides an enterprise-wide research environment that fosters collaboration and communication.

Workers in human resources, finance, and accounts payable at a leading consumer goods company were spending tens of thousands of hours on repetitive, mind-numbing tasks.

Information was stored in various ways and locations across the company, so workers often spent as much or even more time searching 鈥� by writing emails, cold-calling, scrolling internal systems, etc. 鈥� as performing their main tasks.

For example, accountants cannot manage a balance sheet if they don鈥檛 have the necessary numbers for processing outgoing payments, receiving invoices, paying suppliers, updating ledger accounts, and so on. If any of these data languish on a personal computer or in someone鈥檚 inbox, it will take time to track them down.

During the COVID-19 pandemic, employees understandably had more questions 鈥� mostly related to benefits and policies 鈥� for HR and payroll. Most requests arrived through a shared mailbox and were answered by tracking down the appropriate documents and exchanging them over email. This cumbersome process opened the company to security risks and resulted in a backlog of requests, despite all the time invested.

Building a solution: Automated workflows for greater efficiency

Recognizing the need for change, the company reached out to 乌鸦传媒 to request an all-encompassing solution that could improve productivity and reduce labor by streamlining and automating processes across the enterprise.

乌鸦传媒 relied on its expertise with the Microsoft Power Platform, a collection of low-code development tools for building custom platforms, to build a sleek, efficient solution that would modernize business processes from data collection and security to version capturing and reporting.

With a deep knowledge of Power Platform鈥檚 arsenal of tools 鈥� especially Power Apps, Power Automate, Power BI, and Power Virtual Agents 鈥� 乌鸦传媒 worked closely with the company to target pain points and address the monotony of pushing paper, so workers could focus more on what they do best.

Data at one鈥檚 fingertips

乌鸦传媒 used its custom software engineering expertise and Power Platform鈥檚 capabilities to create a secure, cloud-based data collection system that automatically captures item-level version history. The new system rapidly and automatically delivers quick and accurate responses to frequently asked questions. These scalable automated processes, which also benefited from increased data security, provide near real-time reporting to drive insights for continuous improvement and quickly mitigate business risks.

Business teams could standardize their processes by using automation templates and workflows. This effort increased productivity and process accuracy. As a result, HR managers were able to find advanced insights using dashboards that allowed them to refine their processes based on employee feedback.

Better user experiences all-around

The overall experience for the end user has improved immensely thanks to easy-to-use web forms, instant text and email notifications, quick-action cards for approvals, digital signatures for documents, dynamic document generation through standard templates, and more.

The team built a centralized, digital interface to house the entire process of capturing, organizing, and retrieving data. This dashboard makes it easier to retrieve information users need to do their jobs more efficiently. In addition, the project team developed custom apps that run on company-managed mobile devices and support offline mode. If necessary, external users can get secure, temporary access to the app for information. When this happens, simple business-to-business integration using authorized guest accounts for suppliers and outside counsel bypasses the need for a virtual machine or laptop and the associated software licenses, which reduces costs.

It鈥檚 also become easier for auditors to accurately examine the company鈥檚 reporting, thanks to version control, change management, and automatic data retention.

All of these changes mean vital information will no longer get lost on someone鈥檚 personal hard drive, where it is inaccessible to the people who need it.

Unlocking productivity

乌鸦传媒 created automated solutions that handle manual processes, generate reports, and address other frequent pain points that had previously kept workers from focusing on higher priority work. These solutions have saved a substantial amount of time.

A team of three developers deployed around 40 automations that handled over 100,000 requests and saved 30,000 hours over the course of two years. In particular, the self-service chatbot resulted in a 50 percent reduction in HR call volume.

These initial business outcomes are cause for celebration, but they aren鈥檛 the only changes. The introduction of modern digital communications and collaboration tools has resulted in increased user adoption and engagement, and the use of Power Platform has expanded throughout the company. As the company continues its digital business transformation journey, 乌鸦传媒 will provide ongoing support and stands ready to address ongoing needs as they evolve.