"""Lapse & Surrender Study 2023–25 · scelo — Gradio Space. Interactive companion to the Hugging Face dataset ``intelligentactuaries/synthetic-lapse-surrender-study-2023-2025``: 10,000 synthetic policyholders answering the Society of Actuaries (SOA) Research Institute and LIMRA 2023–2025 Term and Whole Life Lapse and Surrender Experience Study data request, expanded into VM-51 policy-year records. Every figure is computed live with the ``scelo`` Python library (``study.py``): experience rates and credibility, the lapse GLM and its lift, and the BasicTerm projection, Solvency II life SCR and IFRS 17 CSM of the in-force term book on a basis set with the controls. The charts are scelo's own chart family (``sc.plot_*``). It runs as a Gradio Space on the Hub, and the organisation's Space ``intelligentactuaries/lapse-surrender-study-scelo`` embeds it; ``python app.py`` runs it locally. """ from __future__ import annotations import base64 import tempfile from dataclasses import asdict from pathlib import Path import gradio as gr import numpy as np import pandas as pd import scelo as sc from scelo import BasicTermAssumptions import study from study import ( DATASET, DATASET_URL, DECREMENTS, EXPERIENCE_BY, FILES, GLM_COVARIATES, MARKETS, PRODUCTS, PUBLISHED_COVARIATES, ROWS, SCELO_DOCS_URL, SOA_2015_22_URL, SOA_REQUEST_URL, YEARS, ) IA_ACCENT = "#A04A1F" CSS = f""" .gradio-container {{ max-width: 1320px !important; margin: 0 auto !important; }} .ia-head h1 {{ font-size: 2.1rem; font-weight: 700; letter-spacing: -0.02em; margin: 0.2rem 0 0.35rem; }} .ia-tagline {{ display: inline-flex; align-items: center; gap: 0.5rem; padding: 0.25rem 0.7rem; border: 1px solid rgba(160, 74, 31, 0.28); border-radius: 999px; background: rgba(160, 74, 31, 0.06); font-size: 0.82rem; color: {IA_ACCENT}; font-weight: 600; }} .ia-tagline .dot {{ width: 6px; height: 6px; border-radius: 50%; background: {IA_ACCENT}; display: inline-block; }} .ia-sub {{ font-size: 0.9rem; opacity: 0.8; margin-top: 0.5rem; }} .ia-links {{ float: right; font-size: 0.9rem; }} .ia-links a {{ margin-left: 1rem; text-decoration: none; opacity: 0.85; }} .ia-synthetic {{ border-left: 3px solid {IA_ACCENT}; background: rgba(160, 74, 31, 0.06); padding: 0.6rem 0.9rem; border-radius: 0 8px 8px 0; font-size: 0.92rem; }} .ia-cards {{ display: grid; grid-template-columns: repeat(auto-fit, minmax(150px, 1fr)); gap: 0.75rem; }} .ia-card {{ background: rgba(160, 74, 31, 0.06); border: 1px solid rgba(160, 74, 31, 0.20); border-radius: 10px; padding: 0.65rem 0.85rem; }} .ia-card-label {{ font-size: 0.7rem; letter-spacing: 0.08em; text-transform: uppercase; font-weight: 600; opacity: 0.8; }} .ia-card-value {{ font-size: 1.45rem; font-weight: 700; margin-top: 0.15rem; }} .ia-card-sub {{ font-size: 0.78rem; opacity: 0.75; margin-top: 0.1rem; }} .ia-card-sub.up {{ color: #1f8f5c; opacity: 1; }} .ia-card-sub.down {{ color: #b43939; opacity: 1; }} .ia-ack {{ border: 1px solid rgba(160, 74, 31, 0.22); border-radius: 10px; padding: 0.9rem 1.1rem; font-size: 0.92rem; line-height: 1.5; }} .ia-footer {{ font-size: 0.8rem; opacity: 0.72; line-height: 1.5; }} .ia-note {{ font-size: 0.85rem; opacity: 0.85; }} .ia-fig {{ margin: 0; background: #ffffff; border: 1px solid rgba(58, 51, 45, 0.12); border-radius: 10px; padding: 0.5rem; }} .ia-fig img {{ width: 100%; height: auto; display: block; }} .ia-cap {{ font-size: 0.8rem; opacity: 0.75; margin-top: 0.35rem; }} """ # Intelligent Actuaries sienna (#A04A1F at 600), on a warm-grey base. SIENNA = gr.themes.Color( c50="#fbf3ee", c100="#f6e4d8", c200="#ecc6ae", c300="#dfa07c", c400="#cf7a4f", c500="#b85f31", c600="#a04a1f", c700="#843c19", c800="#6b3216", c900="#582b14", c950="#301508", name="sienna", ) THEME = gr.themes.Default( primary_hue=SIENNA, neutral_hue="stone", font=[gr.themes.GoogleFont("Source Sans 3"), "ui-sans-serif", "system-ui", "sans-serif"], font_mono=[gr.themes.GoogleFont("IBM Plex Mono"), "ui-monospace", "monospace"], ).set( link_text_color="*primary_600", link_text_color_hover="*primary_700", link_text_color_visited="*primary_600", link_text_color_active="*primary_700", ) # ───────────────────────────────────────────────────────────── # Small helpers # ───────────────────────────────────────────────────────────── def cards(items) -> str: """Metric cards: (label, value, sub, tone) with tone in {"", "up", "down"}.""" cells = [] for label, val, sub, *tone in items: tone_cls = f" {tone[0]}" if tone and tone[0] else "" sub_html = f'
{sub}
' if sub else "" cells.append(f'
{label}
' f'
{val}
{sub_html}
') return f'
{"".join(cells)}
' def chart(fig, caption: str = "") -> str: """A scelo figure as an inline PNG (``sc.save_figure``), sized to its column.""" with tempfile.TemporaryDirectory() as tmp: path = sc.save_figure(fig, Path(tmp) / "figure.png", dpi=150) data = base64.b64encode(Path(path).read_bytes()).decode("ascii") cap = f'
{caption}
' if caption else "" return f'
{caption}{cap}
' def notes_md(notes, basis: str | None = None, extra: list[str] | None = None) -> str: lines = ([f"**Basis:** {basis}"] if basis else []) + [f"· {n}" for n in (notes or []) + (extra or [])] return " \n".join(lines) def text_table(df: pd.DataFrame, formats: dict[str, str]) -> pd.DataFrame: """Format numeric columns as text, leaving missing cells empty and avoiding "-0".""" def fmt(v, f_): if v is None or (isinstance(v, float) and np.isnan(v)): return "" text = f_.format(v) return text[1:] if text.startswith("-") and not text.strip("-0.,%") else text out = pd.DataFrame(df).copy() for c_, f_ in formats.items(): if c_ in out.columns: out[c_] = out[c_].map(lambda v, f_=f_: fmt(v, f_)) return out def as_text(df: pd.DataFrame) -> pd.DataFrame: """Render mixed-type columns (profile summaries, cleaning plans) as text.""" out = pd.DataFrame(df).copy() for c_ in out.columns: if not pd.api.types.is_numeric_dtype(out[c_]) and not pd.api.types.is_bool_dtype(out[c_]): out[c_] = out[c_].map(lambda v: "" if v is None or (isinstance(v, float) and np.isnan(v)) else str(v)) return out def fmt_int(x: float) -> str: return f"{x:,.0f}" def delta(x: float, x0: float, good_up: bool = True) -> tuple[str, str]: d = x - x0 if abs(d) < 0.5: return "published basis", "" tone = "up" if (d > 0) == good_up else "down" return f"{d:+,.0f} vs published basis", tone # ───────────────────────────────────────────────────────────── # Tab logic — every number below is a scelo call in study.py # ───────────────────────────────────────────────────────────── def overview(): vm = study.experience() status = vm["status"].value_counts() html = cards([ ("Policies", fmt_int(vm["agent_id"].nunique()), "one per synthetic policyholder"), ("VM-51 records", fmt_int(len(vm)), "policy × observation year"), ("Exposure", fmt_int(vm["exposure_years"].sum()), "policy-years, 2023–25"), ("Lapses", fmt_int(status.get("lapsed", 0)), None), ("Surrenders", fmt_int(status.get("surrendered", 0)), None), ("Deaths", fmt_int(status.get("died", 0)), "imposed overlay"), ]) fig = chart(sc.plot_rates(vm.assign(lapsed=(vm["status"] == "lapsed").astype(int)), "country", "lapsed", exposure="exposure_years", title="Lapses per exposure-year by market, 2023–25"), "sc.plot_rates · all products · VM-51 policy-years · 95 % Poisson interval") mix = pd.DataFrame(sc.tab(vm, "status", "observation_year")).reset_index() mix.columns = [str(c) for c in mix.columns] products = pd.DataFrame(sc.tab(vm.drop_duplicates("agent_id"), "product")).reset_index() return html, fig, mix, products def experience_view(dec_label, by_label, markets, products, years, cred): by = EXPERIENCE_BY[by_label] statuses = DECREMENTS[dec_label] vm = study.experience() df = vm[vm["country"].isin(markets or []) & vm["product"].isin(products or []) & vm["observation_year"].isin(years or [])] if df.empty: msg = cards([("Records in view", "0", "widen the filters")]) return msg, "", pd.DataFrame(), "No policy-years match the filters.", gr.Dataframe(visible=False), "" events = int(df["status"].isin(statuses).sum()) expo = float(df["exposure_years"].sum()) html = cards([ ("Records in view", fmt_int(len(df)), "VM-51 policy-years after filters"), ("Exposure", fmt_int(expo), "policy-years"), ("Events", fmt_int(events), dec_label), ("Rate", f"{events / expo:.4f}" if expo else "–", "per exposure-year"), ]) fig = chart(sc.plot_rates(df.assign(event=df["status"].isin(statuses).astype(int)), by, "event", exposure="exposure_years", title=f"{dec_label} per exposure-year by {by_label.lower()}"), "sc.plot_rates · bars sorted by rate · 95 % Poisson interval · the vertical line is the rate over " "everything in view") table, notes = study.rates(df, by, statuses) table = text_table(table, {"exposure": "{:,.1f}", "events": "{:,.0f}", "rate": "{:.4f}", "lo_95": "{:.4f}", "hi_95": "{:.4f}"}) table = table.rename(columns={by: by_label, "exposure": "Exposure (policy-years)", "events": "Events", "rate": "Rate", "lo_95": "95 % lower", "hi_95": "95 % upper"}) extra = ["Intervals are exact (Garwood) Poisson intervals on the event count."] if by == "mbti": extra.append("The MBTI spread is a **designed** property of the synthetic population, " "not an empirical finding about personality and lapse.") if statuses == ("died",): extra.append("Deaths come from an imposed Gompertz overlay, q = 0.00004·e^(0.088·age) with sex and " "market factors: they carry no information.") md = notes_md(notes, "sc.freq_sev · central exposure from the VM-51 policy-years", extra) if cred: ct, basis, cnotes = study.credibility(df, by, statuses) ct = text_table(ct, {"weight": "{:,.1f}", "mean": "{:.4f}", "Z": "{:.4f}", "credibility_premium": "{:.4f}"}) return html, fig, table, md, gr.Dataframe(value=ct, visible=True), notes_md(cnotes, basis) return html, fig, table, md, gr.Dataframe(visible=False), "" def glm_view(population, cov_labels, base): covariates = tuple(GLM_COVARIATES[c] for c in (cov_labels or [])) if not covariates: empty = pd.DataFrame() return (cards([("Covariates", "0", "pick at least one")]), "Pick at least one covariate.", "", empty, "", empty, empty, "") model, lift, gini = study.fit_glm(population, covariates, base) base_rate = float(np.exp(model.params["Intercept"])) html = cards([ ("Policies", fmt_int(model.n), "term book" if population == "term" else "all products"), ("AIC", f"{model.aic:,.1f}", None), ("Deviance", f"{model.deviance:,.1f}", f"null {model.null_deviance:,.1f}"), ("Deviance explained", f"{1 - model.deviance / model.null_deviance:.1%}", None), ("Gini", f"{gini:.3f}", "lift of the fitted rate"), ]) lines = [] if population == "term" and base == "first" and sorted(covariates) == sorted(PUBLISHED_COVARIATES): f = study.facts() lines.append( f"✅ **This is the published model.** AIC {model.aic:,.1f} and base rate {base_rate:.4f} match the " f"Scelo IDE run (AIC {f['glm']['ide']['aic']:,.1f}, base {f['glm']['ide']['baseline']:.4f}) and " f"scelo for R (AIC {f['glm']['r']['aic']:,.1f})." ) lines.append(f"`{model.formula}` · {model.family} / {model.link} · offset log(exposure) · engine " f"{model.engine} · base rate exp(β₀) = {base_rate:.4f} lapses per exposure-year") forest = chart(sc.plot_relativities(model), "sc.plot_relativities · exp(β) with 95 % intervals · hollow marker = reference level") rel = text_table(pd.DataFrame(model.relativities()), {"relativity": "{:.3f}", "estimate": "{:.4f}"}) lift_fig = chart(sc.plot_lines(lift.assign(actual=lift["actual"] * 100, predicted=lift["predicted"] * 100), "band", ["actual", "predicted"], title="Lapses per 100 exposure-years, by decile of the fitted rate", subtitle=f"equal-exposure deciles · Gini {gini:.3f}"), "sc.lift · sc.plot_lines · rising = the model ranks lapse; actual ≈ predicted = calibrated") lift_t = text_table(lift, {"exposure": "{:,.1f}", "actual": "{:.4f}", "predicted": "{:.4f}", "lift": "{:.3f}", "a/e": "{:.3f}"}) coef = text_table(pd.DataFrame(model.coef), {"estimate": "{:.4f}", "std_err": "{:.4f}", "z": "{:.2f}", "p_value": "{:.2e}", "exp": "{:.4f}"}) caveat = "" if "mbti" in covariates: caveat = ("ℹ️ The MBTI relativities span about two orders of magnitude, far wider than any real book. " "That spread was **designed into** the synthetic population: do not read it as evidence about " "personality and lapse, and do not use personality (or `community`, `sex`) as a rating factor.") return html, "\n\n".join(lines), forest, rel, lift_fig, lift_t, coef, caveat def value_view(lapse_mode, lapse_custom, disc, mort, acq, maint, prem, ra): crude = study.crude_lapse_rate() lapse = {"illustrative": 0.05, "experience": crude, "custom": lapse_custom}[lapse_mode] basis_label = {"illustrative": "scelo illustrative 5.0 % p.a.", "experience": f"term-book experience 2023–25, {crude:.2%} p.a.", "custom": f"custom {lapse_custom:.2%} p.a."}[lapse_mode] a = BasicTermAssumptions(lapse_rate=lapse, disc_rate=disc, mort_mult=mort, expense_acq_pp=acq, expense_maint_pp_mth=maint, premium_mult=prem) res = study.value(tuple(sorted(asdict(a).items())), ra) ref = study.value(tuple(sorted(asdict(BasicTermAssumptions()).items())), 0.05) pv, pv0 = res["bt"].attrs["pv"], ref["bt"].attrs["pv"] scr, scr0 = res["scr"].attrs["scr"], ref["scr"].attrs["scr"] csm0, csm00 = res["csm"].attrs["csm0"], ref["csm"].attrs["csm0"] loss = res["csm"].attrs["loss_component"] outgo, outgo0 = pv["claims"] + pv["expenses"], pv0["claims"] + pv0["expenses"] items = [ ("PV premiums", fmt_int(pv["premiums"]), *delta(pv["premiums"], pv0["premiums"])), ("PV outgo", fmt_int(outgo), *delta(outgo, outgo0, good_up=False)), ("PV net cash flow", fmt_int(pv["net"]), *delta(pv["net"], pv0["net"])), ("Life SCR", fmt_int(scr), *delta(scr, scr0, good_up=False)), ("Loss component", fmt_int(loss), "IFRS 17: onerous on this basis", "down") if loss > 0 else ("IFRS 17 CSM₀", fmt_int(csm0), *delta(csm0, csm00)), ] caption = (f"Lapse basis: **{basis_label}** · USD · the published basis is scelo's illustrative one " "(5 % lapse, 3 % discount, RA 5 %), on which the Python and R libraries agree to 6.6 × 10⁻¹⁰.") proj = chart(sc.plot_projection(res["bt"], title="BasicTerm projection of the in-force term book"), "sc.basicterm · sc.plot_projection · monthly projection shown by year · the book's own premiums") scr_fig = chart(sc.plot_scr(res["scr"], title="Life SCR build-up"), "sc.scr_life · sc.plot_scr") scr_tab = text_table(pd.DataFrame(res["scr"]).rename_axis("sub-risk").reset_index(), {"charge": "{:,.0f}", "marginal": "{:,.0f}", "share": "{:.1%}"}) csm_fig = chart(sc.plot_csm(res["csm"], title="CSM roll-forward"), "sc.csm · sc.plot_csm") csm_tab = text_table(pd.DataFrame(res["csm"]).head(12), {c_: "{:,.0f}" for c_ in ["coverage_units", "csm_open", "release", "accretion", "csm_close"]}) pack = study.board_pack(res, a, ra, basis_label) f = study.facts()["engines"]["B"] engines = pd.DataFrame( [ ("PV net cash flow · lifelib BasicTerm_ME (lifelib's own basis)", f["ide_lifelib_pv_net"], f["lifelib_pv_net"], "Scelo IDE 0.1.6 and scelo lifelib_run: identical"), ("PV net cash flow · scelo BasicTerm port (illustrative basis)", None, f["port_pv_net"], "Python and R agree"), ("Life SCR", f["ide_scr"], f["lib_scr"], "IDE: lifelib TradLife_A_EX1 on a 579-policy sample · scelo: standard formula, full book"), ("IFRS 17 CSM at initial recognition", f["ide_csm"], f["lib_csm"], "IDE: ifrs17sim on a 392-policy sample, RA 0 · scelo: general model, RA 5 %, full book"), ], columns=["figure", "Scelo IDE", "scelo (Python / R)", "note"], ) engines = text_table(engines, {"Scelo IDE": "{:,.0f}", "scelo (Python / R)": "{:,.0f}"}) return (cards(items), caption, proj, notes_md(res["bt"].notes, res["bt"].basis), scr_fig, scr_tab, notes_md(res["scr"].notes, res["scr"].basis), csm_fig, csm_tab, notes_md(res["csm"].notes, res["csm"].basis), pack, engines) def data_view(name): df = study.load(name) head = (f"### `{name}` · {len(df):,} rows × {df.shape[1]} columns\n" f"`{FILES[name]}` in [{DATASET}]({DATASET_URL}/viewer/{name}) · read with `sc.load`, " "which types columns the way the Scelo IDE types an import. First 200 rows below.") prof = pd.DataFrame(sc.profile(df)) cols = [c_ for c_ in ["column", "type", "count", "missing", "unique", "min", "median", "max", "top_values"] if c_ in prof.columns] prof = prof[cols].copy() for c_ in ["min", "median", "max"]: if c_ in prof.columns: prof[c_] = prof[c_].map(lambda v: "" if v is None or (isinstance(v, float) and np.isnan(v)) else (f"{v:,.4g}" if isinstance(v, float) else str(v))) if name == "term_book": plan_md = ("#### Cleaning plan (`sc.suggest`)\nThe analysts declined all of these: *ENTP → ENTJ* is not " "a typo, capping `face_amount_usd` would destroy the sum-assured tail, and imputing " "`termination_year` onto in-force policies would fabricate lapses.") plan = gr.Dataframe(value=as_text(pd.DataFrame(sc.suggest(df))), visible=True) else: plan_md, plan = "", gr.Dataframe(visible=False) return head, as_text(df.head(200)), as_text(prof), plan_md, plan # ───────────────────────────────────────────────────────────── # Static copy # ───────────────────────────────────────────────────────────── HEADER = f"""
Dataset ↗ scelo docs ↗ SOA request ↗

Lapse & Surrender Study 2023–25

10,000 synthetic policyholders · computed live with scelo
A synthetic answer to the Society of Actuaries (SOA) Research Institute and LIMRA 2023–2025 Term and Whole Life Lapse and Surrender Experience Study data request · Intelligent Actuaries
Synthetic data. Every record was generated by language-model agents, not extracted from any insurer. It describes the generator, not a market: no valuation, pricing or reserving assumption should be set from it. Not SOA or LIMRA data.
""" WHAT = f""" ### What this is The SOA Research Institute and LIMRA asked life insurers for **VM-51 policy-level records** of their individual term and whole life business for 2023–2025. Intelligent Actuaries has no such book, so it generated one: **10,000 synthetic policyholders** in ten markets, each an agent in the Scelo swarm (Claude Code, `claude-sonnet-5`), described the one policy they hold and what happened to it in 2023, 2024 and 2025. A deterministic driver validated every answer, overlaid mortality and wrote the **27,692 policy-year records** in the request's Appendix B layout. This Space loads that dataset from the Hub and recomputes everything with the [`scelo`]({SCELO_DOCS_URL}) Python library: experience rates and credibility, the lapse GLM, and the projection, SCR and CSM of the in-force term book on a basis you choose. """ ACK_CARD = f"""
We gratefully acknowledge the Society of Actuaries Research Institute, the research arm of the Society of Actuaries (SOA), and LIMRA. Their 2023–2025 data request defines the scope, the study window and the VM-51 record layout this dataset imitates, and their 2015–2022 study is the published reference for this experience. This is independent work: not SOA or LIMRA data, and not affiliated with or endorsed by either organisation.
""" ABOUT_LEFT = f""" ### Acknowledgements We gratefully acknowledge the **Society of Actuaries Research Institute**, the research arm of the Society of Actuaries (SOA), and **LIMRA**, who run the Term and Whole Life Lapse and Surrender Experience Study together. - Their [**2023–2025 data request**]({SOA_REQUEST_URL}) (July 2026) defines what this dataset imitates: the scope (level-premium term, annually renewable term, participating and non-participating whole life), the study window (calendar years 2023 to 2025) and the policy-level record layout (the VM-51 format, the request's Appendix B). - Their [**2015–2022 Term and Whole Life Lapse and Surrender Experience Study**]({SOA_2015_22_URL}) (2024), the edition the request updates, is the published reference for this experience. - The record format is the **NAIC Valuation Manual's VM-51**, which the request adopts. **Independence.** This is independent work by Intelligent Actuaries. It is not an SOA or LIMRA publication or dataset, and it is not affiliated with, sponsored by or endorsed by the SOA, the SOA Research Institute, LIMRA or the NAIC. It contains no SOA, LIMRA or contributing-company experience, and the request document (© 2026 Society of Actuaries Research Institute and LIMRA) is not redistributed. Any errors are ours. ### How the data was made 1. **Inversion.** The request asks insurers for VM-51 records; here each agent is one *policyholder*, answering a questionnaire derived from Appendix B. 2. **Design.** 10 markets × 6 attained-age bands × 16 MBTI types × 2 sexes, balanced, seed `20260822`; socio-economic attributes from policyholder-population priors. 3. **Conditioning.** Each agent sees its persona and a dated context block for its own market (August 2023 to August 2026). 4. **Generation.** 10,000 calls to `claude-sonnet-5` through Claude Code on the Scelo swarm's society tier, 22 August 2026. 5. **Validation.** Product-contract rules with every repair logged (79 policies); 11 premiums estimated and flagged. 6. **Mortality overlay.** An imposed Gompertz decrement, so the exposure and cause-04 terminations are complete. 7. **Expansion.** 27,692 VM-51 policy-year records, fixed-width and CSV. ### Limitations - Every statistic describes the generator, not a market. - The MBTI signal is designed in; the mortality is imposed; the design is balanced, not census-weighted (post-stratify before reading anything as a market figure). - Calendar-year timing is narrative-biased: estimate lapse as one force over 2023–25 with an exposure offset. - 94.5 % of agents said they had considered lapsing: an artefact of asking, not a rate. - The free text is model-written and may carry stereotypes about markets and communities. """ ABOUT_RIGHT = f""" ### scelo in this Space Every number is a [`scelo`]({SCELO_DOCS_URL}) call on the published files, computed when you move a control; nothing is precomputed except the cross-engine reference figures. | Stage | Calls | |---|---| | Soft data | `sc.load` · `sc.profile` · `sc.tab` · `sc.suggest` | | Experience | `sc.freq_sev` · `sc.credibility` · `sc.plot_rates` | | Pricing | `sc.glm` · `.relativities()` · `sc.lift` · `sc.gini` · `sc.plot_relativities` · `sc.plot_lines` | | Valuation | `sc.basicterm` · `sc.scr_life` · `sc.csm` · `sc.plot_projection` · `sc.plot_scr` · `sc.plot_csm` | | Hard data | `sc.hard` · `sc.report` | ```bash pip install scelo ``` ### Cite ```bibtex @misc{{denewade2026syntheticlapse, author = {{Denewade, Ali}}, title = {{Synthetic Term and Whole Life Lapse and Surrender Study, 2023--2025: 10,000 synthetic policyholders in the VM-51 layout}}, year = {{2026}}, publisher = {{Hugging Face}}, howpublished = {{\\url{{{DATASET_URL}}}}}, note = {{Synthetic data, generated in response to the SOA Research Institute and LIMRA 2023--2025 Term and Whole Life Lapse and Surrender Experience Study data request. Not SOA or LIMRA data.}} }} ``` Contact: [research@intelligentactuaries.com](mailto:research@intelligentactuaries.com) · [intelligentactuaries.com](https://intelligentactuaries.com) """ FOOTER = f"""
Synthetic data generated by Intelligent Actuaries in response to the SOA Research Institute and LIMRA 2023–2025 Term and Whole Life Lapse and Surrender Experience Study data request, with thanks to both organisations. Not SOA or LIMRA data; not affiliated with or endorsed by the SOA or LIMRA. Computed with scelo {sc.__version__}. Code MIT · data CC BY 4.0.
""" MARKET_CHOICES = [(f"{c} · {n}", c) for c, n in MARKETS.items()] # ───────────────────────────────────────────────────────────── # Layout # ───────────────────────────────────────────────────────────── with gr.Blocks(title="Lapse & Surrender Study · scelo", theme=THEME, css=CSS, delete_cache=(3600, 3600)) as demo: gr.HTML(HEADER) with gr.Tabs(): # Overview ------------------------------------------------------------------ with gr.Tab("🧭 Overview"): ov_cards = gr.HTML(cards([("Loading", "…", "reading the dataset from the Hub")])) with gr.Row(): with gr.Column(scale=3): ov_plot = gr.HTML() ov_mix = gr.Dataframe(label="Status by observation year · sc.tab", interactive=False) with gr.Column(scale=2): gr.Markdown(WHAT) gr.HTML("

Acknowledgement

" + ACK_CARD) ov_products = gr.Dataframe(label="Products · sc.tab", interactive=False) # Experience ---------------------------------------------------------------- with gr.Tab("📉 Experience") as tab_exp: with gr.Row(): with gr.Column(scale=1, min_width=270): x_dec = gr.Dropdown(list(DECREMENTS), value="Lapse", label="Decrement") x_by = gr.Dropdown(list(EXPERIENCE_BY), value="Market", label="Group by") x_markets = gr.CheckboxGroup(MARKET_CHOICES, value=list(MARKETS), label="Markets") x_products = gr.CheckboxGroup(PRODUCTS, value=PRODUCTS, label="Products") x_years = gr.CheckboxGroup(YEARS, value=YEARS, label="Observation years") x_cred = gr.Checkbox(False, label="Bühlmann–Straub credibility (sc.credibility)") with gr.Column(scale=3): x_cards = gr.HTML() x_plot = gr.HTML() x_table = gr.Dataframe(label="Occurrence / exposure · sc.freq_sev", interactive=False) x_notes = gr.Markdown(elem_classes="ia-note") x_cred_table = gr.Dataframe(label="Credibility-weighted rates", interactive=False, visible=False) x_cred_notes = gr.Markdown(elem_classes="ia-note") # Lapse GLM ----------------------------------------------------------------- with gr.Tab("🧮 Lapse GLM") as tab_glm: with gr.Row(): with gr.Column(scale=1, min_width=270): g_pop = gr.Radio([("Term book 2023–25 (4,254) · the published model", "term"), ("Every policy, all products (10,000)", "all")], value="term", label="Population") g_cov = gr.CheckboxGroup(list(GLM_COVARIATES), value=["Market", "Product", "MBTI type"], label="Covariates") g_base = gr.Radio([("Alphabetically first (as published)", "first"), ("Most frequent level", "frequent")], value="first", label="Reference level") gr.Markdown("Poisson GLM, log link, offset log(exposure) · `sc.glm`", elem_classes="ia-note") with gr.Column(scale=3): g_cards = gr.HTML() g_tie = gr.Markdown(elem_classes="ia-note") g_forest = gr.HTML() with gr.Row(): g_rel = gr.Dataframe(label="Relativities", interactive=False, max_height=460) g_lift = gr.HTML() with gr.Accordion("Lift table and coefficient table", open=False): g_lift_t = gr.Dataframe(label="Lift (equal-exposure deciles)", interactive=False) g_coef = gr.Dataframe(label="Coefficients", interactive=False) g_caveat = gr.Markdown() # Pricing & valuation ------------------------------------------------------- with gr.Tab("💰 Pricing & valuation") as tab_val: with gr.Row(): with gr.Column(scale=1, min_width=270): v_mode = gr.Radio([("scelo illustrative · 5.0 % p.a.", "illustrative"), ("Term-book experience 2023–25 · 12.97 % p.a.", "experience"), ("Custom", "custom")], value="illustrative", label="Lapse basis") v_custom = gr.Slider(0.0, 0.40, value=0.08, step=0.005, label="Custom annual lapse rate", interactive=False) v_disc = gr.Slider(0.0, 0.08, value=0.03, step=0.0025, label="Discount rate (p.a.)") v_mort = gr.Slider(0.5, 2.0, value=1.0, step=0.05, label="Mortality multiplier") v_acq = gr.Slider(0.0, 500.0, value=100.0, step=10.0, label="Acquisition expense per policy") v_maint = gr.Slider(0.0, 20.0, value=5.0, step=0.5, label="Maintenance expense per policy-month") v_prem = gr.Slider(0.5, 1.5, value=1.0, step=0.05, label="Premium multiplier") v_ra = gr.Slider(0.0, 0.20, value=0.05, step=0.01, label="IFRS 17 risk adjustment (% of PV claims)") gr.Markdown("In-force term book at 31 Dec 2025 · 2,698 model points", elem_classes="ia-note") with gr.Column(scale=3): v_cards = gr.HTML() v_caption = gr.Markdown(elem_classes="ia-note") v_proj = gr.HTML() v_proj_notes = gr.Markdown(elem_classes="ia-note") with gr.Row(): with gr.Column(): v_scr = gr.HTML() v_scr_t = gr.Dataframe(interactive=False, show_label=False) v_scr_notes = gr.Markdown(elem_classes="ia-note") with gr.Column(): v_csm = gr.HTML() v_csm_t = gr.Dataframe(interactive=False, show_label=False) v_csm_notes = gr.Markdown(elem_classes="ia-note") gr.Markdown("#### Board pack\n`sc.hard` stamps each table with a content hash and its audit " "entries; `sc.report` assembles them, with the acknowledgement and the " "synthetic-data statement, into a printable board pack.") v_pack = gr.DownloadButton("⬇️ Download the board pack (HTML)", variant="secondary") with gr.Accordion("The same book on the other engines (published results)", open=False): v_engines = gr.Dataframe(interactive=False, show_label=False) gr.Markdown("From `analysis/facts.json` in the dataset. The sampled IDE figures are " "comparable for scale, not for identity.", elem_classes="ia-note") # Data ------------------------------------------------------------------------ with gr.Tab("🗂️ Data") as tab_data: with gr.Row(): with gr.Column(scale=1, min_width=270): d_name = gr.Radio([(f"{n} · {ROWS[n]:,}", n) for n in FILES], value="term_book", label="Config") gr.Markdown(f"[Open the dataset on Hugging Face ↗]({DATASET_URL})") with gr.Column(scale=3): d_head = gr.Markdown() d_preview = gr.Dataframe(interactive=False, max_height=380, show_label=False) gr.Markdown("#### Column profile (`sc.profile`)") d_profile = gr.Dataframe(interactive=False, max_height=420, show_label=False) d_plan_md = gr.Markdown() d_plan = gr.Dataframe(interactive=False, visible=False, show_label=False) # About ----------------------------------------------------------------------- with gr.Tab("📜 About"): with gr.Row(): with gr.Column(scale=3): gr.Markdown(ABOUT_LEFT) with gr.Column(scale=2): gr.Markdown(ABOUT_RIGHT) gr.HTML(FOOTER) # Events: each tab fills on first view, then follows its controls. ---------------- demo.load(overview, outputs=[ov_cards, ov_plot, ov_mix, ov_products]) x_in = [x_dec, x_by, x_markets, x_products, x_years, x_cred] x_out = [x_cards, x_plot, x_table, x_notes, x_cred_table, x_cred_notes] gr.on([tab_exp.select] + [c.change for c in x_in], experience_view, x_in, x_out) g_in = [g_pop, g_cov, g_base] g_out = [g_cards, g_tie, g_forest, g_rel, g_lift, g_lift_t, g_coef, g_caveat] gr.on([tab_glm.select] + [c.change for c in g_in], glm_view, g_in, g_out) v_in = [v_mode, v_custom, v_disc, v_mort, v_acq, v_maint, v_prem, v_ra] v_out = [v_cards, v_caption, v_proj, v_proj_notes, v_scr, v_scr_t, v_scr_notes, v_csm, v_csm_t, v_csm_notes, v_pack, v_engines] v_mode.change(lambda m: gr.Slider(interactive=m == "custom"), v_mode, v_custom) v_sliders = [v_custom, v_disc, v_mort, v_acq, v_maint, v_prem, v_ra] gr.on([tab_val.select, v_mode.change] + [s.release for s in v_sliders], value_view, v_in, v_out) d_out = [d_head, d_preview, d_profile, d_plan_md, d_plan] gr.on([tab_data.select, d_name.change], data_view, [d_name], d_out) demo.launch()