Pinnacle Award Finalists
Congratulations to the AFP 2026 Pinnacle Award Finalists
Bandwidth, Google and ServiceNow have been named the AFP 2026 Pinnacle Award Finalists. Now it’s your turn to help decide who will be the AFP 2026 Pinnacle Award Grand Prize Winner.
Cast your vote by September 18 and recognize the solution you think stands above the rest. Learn more about each finalist’s solution:
Problem
Bandwidth's treasury team relied on a legacy treasury management system that was connected only to its primary banks and required extensive manual effort to consolidate data across entities, currencies and banking relationships. As the company grew, daily cash positioning, forecasting and liquidity analysis became increasingly dependent on manual exports and spreadsheets. Adding new functionality required purchasing and implementing additional TMS modules, creating what the team described as a "modular tax," while the system's lack of an API-first architecture limited automation and AI capabilities.
Solution
Instead of purchasing a new TMS, Bandwidth built its own AI-native treasury platform. Using Atlar's bank connectivity platform and Claude through Atlar's MCP connector, the treasury team established real-time API connectivity across its banking relationships and centralized live bank data in a single environment. The team developed a real-time cash-positioning dashboard providing a single view of all entities and banking partners, along with automated cash flow forecasting, transaction categorization, liquidity analysis, multilateral netting, debt monitoring and covenant tracking, and bank fee analysis. Each capability was designed to align with the company's treasury policies and liquidity management framework.
Impact
The platform replaced manual data gathering, exports and spreadsheet-based analysis with real-time, connected treasury data. Processes that previously required hours of collecting and reconciling information now occur automatically. Treasury gained a consolidated view of liquidity across the organization, automated forecasting and analysis capabilities, streamlined intercompany and debt management processes, and continuous visibility into banking costs. The result is a treasury intelligence platform that allows the team to spend less time assembling data and more time making liquidity and capital allocation decisions.
👉 Cast Your Vote for Bandwidth
Problem
Google's investment process was highly manual and difficult to scale. Cash managers had to forecast cash across hundreds of accounts and entities, negotiate deposit terms with banks through phone calls and emails, and manually manage placements, confirmations and trade bookings. These processes made it difficult to compare opportunities across markets, identify investable cash quickly, source the best rates, and consistently execute investments while minimizing delays and errors.
Solution
Google built an automated investment workflow on top of its existing SWIFT connectivity, MT320 messaging infrastructure, banking integrations and ERP systems. Machine-learning models generate daily cash forecasts, calculate investable cash and provide liquidity outlooks across multiple markets. AI agents continuously collect rates from multiple counterparties through channels such as emails and rate feeds, then evaluate yield, counterparty limits and liquidity horizons to generate investment recommendations. An execution agent places trades and automatically creates the related ERP entries. Google piloted the solution with HSBC in India and Taiwan, where cash forecasting, electronic rate collection, investment recommendations and automated transaction processing were tested following treasury manager approval.
Impact
The new process transformed a fragmented, manually driven investment workflow into a more automated and standardized operation. Treasury managers gained automated cash forecasts, visibility into investable cash and liquidity positions, and AI-generated recommendations, eliminating the need to manually gather and analyze data. Investment instructions can now flow through Google's ERP and SWIFT infrastructure, with confirmations and execution occurring through the same secure channel. The solution also reduced reliance on manual bank reconciliations, streamlined investment execution, and created a more consistent investment process using Google's existing treasury and banking infrastructure.
Problem
As ServiceNow expanded globally, its treasury team assumed responsibility for liquidity, investments, forecasting, capital planning, working capital financing, insurance and financial risk across more than 50 countries. However, the team lacked a single trusted data source that connected its systems, entities, banks and accounts. As a result, they spent 40 to 60 hours each week manually collecting, reconciling, validating and distributing information before analysis could begin. They recognized that AI alone would not solve these challenges because fragmented, inconsistent data would produce unreliable results.
Solution
ServiceNow first built an in-house treasury data architecture that aggregated and standardized information from more than 10 source systems into a centralized repository, creating a single source of truth for cash, investments, forecasts, foreign exchange data and bank relationships. The team then developed real-time dashboards and integrated Tradeweb ICD Portal and Clearwater to provide holding-level security data and exposure monitoring. On top of this foundation, they deployed an LLM-based chatbot, an ML-based accounts payable cash forecasting model, automated FX hedging across more than 40 currency pairs, and four production workflows covering bank account management, ACH direct debit authorization, general ledger integration and cash forecast collection. They also built a structured knowledge layer containing entity relationships, policy constraints and operational procedures.
Impact
The initiative transformed treasury from a function heavily dependent on manual data gathering into one operating from a centralized, connected data environment. Treasury gained real-time visibility into cash, investments, forecasts, and counterparty exposures through a single source of truth. Automated dashboards, forecasting models, workflow automation, and AI-enabled tools replaced many manual processes and provided faster access to actionable insights. Integrated Tradeweb and Clearwater data also enabled AI-powered monitoring of counterparty exposures, allowing treasury to track market news against actual investment positions and receive alerts and recommendations. By establishing a strong data foundation before deploying AI, ServiceNow improved data consistency, visibility and automation across its global treasury operations.
👉 Cast Your Vote for ServiceNow