Income Lab launches AI paraplanner to tackle advisor capacity constraints

Income Lab, a retirement planning platform founded by former Jackson National executives Johnny Poulsen and Justin Fitzpatrick, has launched Penny, an AI paraplanner designed to assist financial advisors with complex client tasks. The tool integrates a natural language interface with deterministic calculation engines, a combination the founders say is intended to minimise the risk of "fuzzy math" or hallucinations often associated with generative AI models.

The launch marks a significant evolution for the firm, which was established more than eight years ago with the goal of making retirement planning dynamic rather than static. Over the past 12 to 18 months, advanced artificial intelligence capabilities have increasingly impacted wealth management, allowing firms to develop tools that were previously considered impractical due to development costs.

Penny is specifically designed to handle areas where many advisors lack specialised expertise, including Medicare planning, estate planning, and income management. By automating these processes, the platform aims to reduce or eliminate the need for manual data entry, a task that has traditionally required advisors to port data between disconnected platforms and spend significant time typing information into software interfaces.

In one cited example, an advisor in Des Moines, Iowa, used the platform to review the financial situation of a client who had recently retired. The client, who had previously earned $600,000 a year but now has taxable income of less than $150,000, was facing high Medicare surcharges based on income from two years prior. The tool automatically flagged that the client’s retirement constituted a qualifying life-changing event, allowing for an appeal of the Income-Related Monthly Adjustment Amount (IRMAA) surcharges. In this instance, the process reportedly saved the client approximately $14,000.

The introduction of Penny also addresses broader industry concerns regarding capacity constraints and a potential shortage of financial advisors as older professionals leave the field. By enabling advisors to move from high-level strategic planning to tactical advice implementation, the technology aims to allow firms to serve more clients efficiently without sacrificing the quality of the advisor-client relationship.

Poulsen noted that the current pace of innovation is comparable to the introduction of Monte Carlo planning in the late 1990s, suggesting that AI will continue to deepen the value created within the advisory sector. As development costs for new capabilities decline, the firm expects to expand its suite of AI-driven tools further.