Implementation Notes

Status: Draft, 2026-08-20. Built from technical-notes.md; the product those notes describe is specified in product-spec.md.

This is a mechanics demonstration, not a pricing or reserving result. The contractual mechanics are sourced: attained-age repricing at renewal (kōshin, 更新) with no health declaration (kokuchi, 告知), truncation at the renewal ceiling into an 80歳満了 term, the to-a-stated-age (sai manryō, 歳満了) shape never renewing, the absence of any surrender value (kaiyaku-henreikin, 解約返戻金) and hence of automatic premium loan (jidō furikae kashitsuke, 自動振替貸付), and the living-needs acceleration (a tokuyaku, rider — リビング・ニーズ特約) discount and per-insured cap. So, unusually for this repository, is the premium: carriers publish rate cards, and the anchor cell’s ¥974 a month is a published figure [S2]. Everything else is std — the mortality factor, the lapse curve, the renewal-decline rate, the expense and commission levels, the premium scale above age 50 — and mort_table.csv is a documented proxy for 生保標準生命表2018（死亡保険用）, not that table. Replace it all with company data before drawing any conclusion from the numbers.

Run it

python products/term_life/run.py
python products/term_life/run.py 7        # another model point

Three lines to the same thing:

import modelx as mx
model = mx.read_model("products/term_life/Term_JP_S")
model.Projection[1].result_cf()

Projection takes a point_id; Projection[1] is the worked-example anchor cell. result_cf() returns a DataFrame indexed by the projection month t, one column per cash flow line; result_pols() prints the counts, decrement rates, term index and premium beside them, which is where a renewal boundary becomes legible — the row whose decline_rate is non-zero and whose prem_pp changes on the next row. model.Projection.doc carries the notes’ symbols mapped to the cells names.

The time index is 0-based and counts months

t follows the library-wide convention and its unit is the policy month: t = 0 is the first projected month, the frame is range(proj_len()), the last index is proj_len() - 1, and len(result_cf()) == proj_len(). proj_len() is therefore the number of projected months, not the last index; on the anchor cell it is 600 — fifty years, twelve rows each — and result_cf() is indexed 0 .. 599. Attained age is age(t) = age_at_entry() + t // 12, so age(0) is the 契約年齢 itself and holds for the twelve months of policy year 1, and pols_if(0) == pols_if_init() == 1.

The contractual policy year is the 1-based label policy_year(t) = 1 + t // 12, and duration(t) = t // 12 is the count of completed policy years; both are derived, never indexed by. Two 1-based labels stay 1-based because they are contractual rather than positional — the term index term_index(t) = 1 + duration(t) // policy_term() (k = 1 in the original 保険期間) and the policy_year key of lapse_table.csv, which lapse_rate(t) reads at policy_year(t).

The renewal boundaries — the last month of each 保険期間 — are t = 119, 239, 359, 479 on the anchor cell, i.e. (t + 1) % term_months() == 0 where term_months() = 12 n, and the repriced premium is first charged on the following row. pols_if(t) is additionally defined at t = proj_len(), one row beyond the frame, where it is the survivors whose cover expires at the ceiling (pols_if(600) = 0.026042 on the anchor cell).

result_cf() reads back as an annual statement with df.groupby(df.index // 12).sum().

What the monthly grid changed, and what it did not

The contract is quoted in years — the 保険期間, the ceiling, the lapse curve, the mortality table — so the monthly step is finer than the guarantees rather than finer than the product. Four things follow.

Rates convert; one-off proportions do not. mort_rate and lapse_rate are the annual rates the tables are stated on, and mort_rate_mth and lapse_rate_mth are the decrements actually applied, on the effective convention 1 - (1 - r)^(1/12). decline_rate is neither: it is the proportion of survivors who walk away when a 保険期間 ends, applied unconverted in that one month.

pols_if at the anniversaries did not move. Because the conversion is the effective one, pols_if(12 j) here equals the annual-step model’s pols_if(j) to floating point — l(120) = 0.466683 and l(600) = 0.026042, the same two numbers. Every difference between the two runs is a difference of cash flow timing, never of decrement basis.

premium_mode became live. The annual model annualized every mode into one start-of-year payment and carried the column inert; prem_mode_months() and prem_due_pp(t) now pay 月払 monthly, 半年払 every sixth month and 年払 every twelfth. prem_pp survives as the annualized pricing quantity — the repricing rule, the commission scale and the renewal ladder are all quoted per annum — and is no longer the cash flow.

One std was retired rather than restated. The annual model collected a whole year’s premium in advance from lives that might exit in month two and offset that against claims booked at the end of the year, declaring the pair matched. Neither approximation survives here, so neither offset is needed — and the anchor cell’s undiscounted premium falls from ¥470,348.54 to ¥457,507.04 as a result.

CSV time-like columns

File	Column	Decision
lapse_table.csv	policy_year (1 … 5)	Unchanged. A contractual 1-based policy-year label, not the frame’s t; lapse_rate(t) maps through policy_year(t) = 1 + t // 12 and carries the last row forward. The rates stay annual; lapse_rate_mth(t) derives the month’s decrement
mort_table.csv	age (20 … 80)	Unchanged. Attained age, not a time index; read through age(t)
prem_rate_table.csv	issue_age, term_y	Unchanged. An entry age and a term length, not points on the frame
model_point_table.csv	issue_age, term_y, expiry_age, renew_ceiling	Unchanged. Ages and term lengths. The table carries no duration, in-force offset or frame-position column — every model point is projected from issue at t = 0

No CSV value changed in the conversion to the monthly grid either. Every input is quoted in the unit the contract or the source states it in — an annual rate, an attained age, a term in years — and the conversion to a month happens in the projection, once, where it can be named.

The horizon is the renewal ceiling, not the term

This is the structural difference from the UK and U.S. term models in this repository, not a parameter difference. A fixed-year (nen manryō, 年満了) 更新型 contract renews automatically at the end of every policy term (hoken kikan, 保険期間) unless the policyholder gives notice, with no 告知 and no fresh underwriting, and the premium is recomputed on attained age at the scale then in force [S1] [S4] [S8] [S12]. So proj_len() is 12 × (renew_ceiling() - age_at_entry()), not 12 × policy_term(): a ten-year term issued at age 30 is projected for fifty years — 600 months — across five separately priced terms.

Three cells carry it. term_index(t) is the notes’ k, the state variable a Japanese term model needs and a UK one does not, because the premium is a function of the term rather than of the projection year; term_start_age(k) is the attained age the term is priced at; and term_len(k) is min(n, w_r - x_k), where truncation at the ceiling lives. A renewal that would carry the policy past attained age 80 renews as an 80歳満了 term instead [S1] [S2] [S8], so an issue age of 35 has a final term of five years, priced over its own five, and the projection still ends exactly at 80 — model point 4. Truncation shortens the term, not the horizon; three other market rules exist [S4] [S7] [S12] and importing one would change it.

A 歳満了 contract never renews [S1]: term_index is 1 and decline_rate zero in every month, and proj_len() is 12 × the term. Model points 3, 6 and 9 are 歳満了.

Nothing else resets at a boundary. pols_if is continuous across it; no acquisition expense and, in the base run, no commission is paid; and the suicide and contestability clocks run from the original risk commencement date (sekinin kaishi bi, 責任開始日) and do not restart [S1] [S4] [S7] [S8] — only reinstatement (fukkatsu, 復活) restarts them [S1]. Neither clock is monetized, so neither is a cells, but treating a renewed term as a fresh policy gets persistency, the strain pattern and both clocks wrong at once.

Renewal decline is its own decrement

decline_rate(t) is non-zero only in a boundary month, and the lives it removes are taken after mortality and after ordinary lapse — the notes’ steps 3, 4 and 5, exposed as pols_if_at(t, "BEF_LAPSE") and pols_if_at(t, "BEF_DECLINE"). In the month it applies it dwarfs the other two: at t = 119 on the anchor cell it removes 0.08235591 of the 0.08474749 lives that leave, 97.2% of that month’s exits. The annual grid could only say 74%, because it was comparing one renewal decision with twelve months of ordinary lapse. Folding it into lapse_rate makes the boundary invisible and mis-times most of the cohort’s departure, so it has its own proportion, its own count (pols_decline) and its own term in the roll-forward check — and it is never converted to a monthly rate, because it is a decision taken on a date.

Two behaviours roll into the one rate: the policyholder who gives notice, and the one whose first renewed premium goes unpaid through grace, where the renewal is treated as never having happened and the contract terminates at the original expiry [S1] [S7]. Only the first is a decision. Both leave at the boundary, and neither may appear in force at t + 1 collecting the renewed premium — which is what the processing order enforces. The monthly grid makes the second representable for the first time, since grace is about a month long [S1] [S8]; separating them would need a take-up assumption the sources do not give, so the composite keeps one rate.

One decrement, one benefit

生保標準生命表2018（死亡保険用）includes 高度障害 (kōdo shōgai, severe disability) inside its death rate REG-R20, and the contract pays one sum assured and terminates on whichever event becomes payable first [S1] [S8]. mort_rate(t) is therefore the combined decrement and there is no disability incidence anywhere in this model; adding one on top of the table double-counts the benefit, the notes’ first-listed pitfall and the same shape as the terminal-illness ruling in the UK term model. The リビング・ニーズ特約 module follows the rule too: an acceleration is a re-timing and re-pricing of the death benefit, not a second claim, so ln_share(t) splits the decrement rather than adding to it.

The premium chassis is mostly sourced

Japanese carriers publish rate cards, so the structure decomposes exactly [S2]:

P_m(k) = f + r(sex, x_k, m_k) * SA / 5,000,000
P_a(k) = 12 * P_m(k)

with f = 248 per month and P_m rounded half up to the whole yen, which is the granularity rate cards are quoted at [S2] [S9] [S10]. On this grid P_m is the cash flow and P_a the pricing quantity; prem_mode_months() decides how many months’ worth of P_m fall in a given month. Four cells are sourced — male ages 30, 40 and 50 and female age 30, all at a ten-year term — and prem_rate_m() uses the published cell wherever one exists. Ages 60 and 70 are published by no carrier and the anchor cell reaches both, so the extension off the is_anchor row of the matching sex is unavoidable rather than optional:

r(sex, x, m) = r_anchor * qbar(x, m) / qbar(x_a, m_a)

Applied where a published cell already exists it back-casts to ¥958.9 a month at age 30 against the published ¥974 (−1.5%) and ¥1,806.4 at age 40 against ¥1,823 (−0.9%); it gives ¥8,976 at 60 and ¥23,881 at 70. That is reassuring about the form of the scale and says nothing about the level an insurer will charge decades out — the notes’ third-largest lever. qbar averages table rates, through mort_table_mean(), never the best-estimate mort_rate(): feeding the 0.80 factor into a rate card would move it by an assumption unrelated to pricing. The ¥248 is likewise a premium component and not an expense recovery [S2]; it enters the model only through prem_pp(), and crediting it against expenses() counts it twice.

claims_lapse is a column of zeros, deliberately

There is no 解約返戻金 and no paid-up value at any duration on this composite [S1] [S4] [S6] [S8] [S9] [S10] [S13] [S14], so an ordinary lapse is a pure decrement: it moves pols_if and pays nothing. claims(t, "LAPSE") exists, returns zero and gets a column in result_cf(), because a non-zero lapse row imported from a model with cash surrender values is one of the notes’ pitfalls — and because one of the eight carriers whose position is documented does write this design with a surrender value [S12]. A Japan term chassis cannot assume the absence the way a UK one can, so the zero is asserted from sources, not from the class.

There is no 自動振替貸付, stated in terms by one carrier [S7], and no collateral for a policy loan (keiyakusha kashitsuke, 契約者貸付) either — the second being an inference from the missing surrender value rather than a citation, since that same carrier points its policyholders at the 契約貸付制度 [S7] and the document appearing to rule the policy loan out could not be extracted [S11]. Importing the APL mechanic that the whole life technical notes (終身保険) carries would create a no-lapse cushion this contract does not have; grace, then force-out (shikkō, 失効), then 復活-or-not is the whole persistency machinery.

Inputs are external files

The model folder holds __init__.py, _system.json and the two Space directories, and nothing else — no _data/, no IOSpec, no embedded values. The four CSVs live beside run.py: the annuallife/TradLife_A layout rather than basiclife/BasicTerm_S’s, so a diff of the model shows logic changes only.

File	Contents	Provenance
model_point_table.csv	Nine points. Point 1 is the anchor cell (M30 / 年満了 10年 / ceiling 80 / ¥10,000,000, which the premium scale prices at ¥974 a month); the rest carry the female rate cell, both 歳満了 shapes, ceiling truncation, the current_term boundary, all three riders and both ends of the issue-age and sum-assured envelopes. No premium column — the premium is looked up, not stored, which is what makes the repricing at 更新 fall out of the same lookup	the cells are std; the price they resolve to at issue is [S2]
mort_table.csv	Table qx by sex and attained age 20–80, 122 rows	the canonical jplib std construction, shared across the library, anchored on rates quoted from REG-R18 R4 and log-linearly interpolated between them
lapse_table.csv	Annual lapse by contractual, 1-based policy year (read at policy_year(t)), 9 / 7 / 6 / 5.5 / 5 percent, last row carried forward	std shape; level reconciled to REG-R31
prem_rate_table.csv	Marginal monthly rate per ¥5,000,000 and the ¥248 flat element, four cells, is_anchor marking one row per sex	[S2] rate cards, decomposed row by row in provenance

Every assumption row carries a provenance column with its tag and, in the premium table, the arithmetic of the decomposition. The trade-off: the model is not portable on its own. Copying Term_JP_S/ without its parent’s CSVs gives a model that reads and then fails on first evaluation.

Read once, in Data

Projection is parameterized by point_id, so every Projection[N] is a separate ItemSpace with its own cells cache, and readers placed there would re-read every file for every model point. They live in the unparameterized Data Space instead, where each file is read once per model. Data.input_dir() resolves to _model.path.parent at run time, so the model works from any checkout; prem_anchor_table() is derived from prem_rate_table() rather than read from a fifth file.

The mortality table is a proxy, and says so

生保標準生命表2018（死亡保険用）is published by 日本アクチュアリー会 free and in full at a stable public URL REG-R18 R3 R4 — anyone can retrieve it and check a rate, the sharpest contrast in this repository with the UK term model, whose CMI tables cannot be read at all without a subscription. But the publisher’s site terms prohibit reproduction and transmission without written consent REG-R21, so the library must not ship a copy.

mort_table.csv is therefore a std construction: the canonical jplib proxy, one file shared by every product in this library rather than a per-product reconstruction, so that a cell carries the same rate and the same provenance wherever it is shipped. Its anchors are the union of the rates read from the published table across the library’s research passes REG-R18 — more ages than this product’s own pass read R4 — and every other age is log-linear in ln q between its two neighbouring anchors, at the published table’s own five-decimal granularity, with no extrapolation anywhere. Each row’s provenance says which of the two it is. The rows shipped here run from attained age 20 to attained age 80, the range this product’s model points can reach. The anchoring is what makes the model reproduce the worked example’s rates exactly; nothing about Japanese mortality should be read off the interpolated rows. The shipped rates are also table rates: mort_be_factor is applied in Projection to reach a best-estimate basis, because 標準生命表2018 is a valuation table carrying a risk-theory margin sized near 2σ and capped at 130% of the unadjusted rate REG-R20.

Modules that are off in the base run

Eight of the notes’ optional constructions are implemented and switched off, so the base run reproduces the worked example while the machinery stays visible and testable. Three are model point columns and five are References on Projection.

Module	Switch	Off value	On where	What it does
リビング・ニーズ特約	living_needs	0	points 6, 9	Accelerates the death benefit at A − A i_ln / 2 − six months’ premiums on A [S1] [S7] [S8] [S12], as a share of the existing decrement. Barred within a year of a non-renewable expiry [S1] [S7] — on this grid the last twelve months, t ≥ proj_len() - 12, which is the clause itself rather than the annual grid’s one-row approximation of it
保険料の払込の免除	wop	0	point 7	A two-state chain on the premium-paying population, on an accident producing a 別表4 state within 180 days [S1] [S8] [S12] [S14] — a much lower bar than the 別表3 test for 高度障害, so it does not reuse mort_rate()
復活	reinstatement	0	point 8	The lapsed-but-reinstatable pool and its three-year window [S1], tracked by vintage — 36 monthly vintages, where the annual grid had three: the window runs from each life’s own 失効, and one blanket balance drops a cohort early or late
Contract boundary	contract_boundary	ceiling	point 5	current_term truncates at the end of the 保険期間 in force at the valuation date. The two answers have opposite signs, +¥47,254.64 against −¥16,071.24, and the ESR treatment settling it is unverified here REG-R16
Selective lapsation	sel_lapse_lambda	0.0	—	q_eff = q (1 + λ max(0, 1 − l(t)/l_ref)), with sel_lapse_ref = 1.0 std so the reference block is the cohort at issue. One-directional: renewal takes no 告知 [S1] [S4] [S8] [S12], so an uninsurable life renews while a healthy one re-shops, four times over on the anchor cell
Renewal-decline elasticity	decline_beta	0.0	—	d = min(d_max, d_0 (P_a(k+1)/P_a(k))^β), the flat 15% at zero. The jump it responds to accelerates: ×1.87, ×2.16, ×2.28, ×2.66, so decline_max = 0.50 std binds at β = 2 on every boundary and at β = 1 on none
Age-basis shift	mort_age_shift	False	—	q_x → sqrt(q_x q_(x+1)). 契約年齢 is age last birthday (man-nenrei, 満年齢) [S1] and the table is built for age nearest birthday (hoken-nenrei, 保険年齢) REG-R20, so the base run reads half a year early and understates. The shift moves q up — 0.73% at age 30, 4.15% at 40 — and one moving it down has the sign wrong
Commission at 更新	comm_new_term_rate	0.0	—	Pays acquisition commission in the first month of each renewed term. A 更新 is not new business [S1] [S4], but no document discloses a commission scale at all, so the zero is a choice — and a first-year scale would flip the sign at t = 120, 240, 360 and 480

Two scope limits are stated rather than approximated. The 復活 arrears at 年6% compound [S1] are not monetized: they settle premiums for years in which this projection collected none, and recognizing them needs a missed-premium ledger the notes do not specify [std scope]. And a partial acceleration leaves a reduced contract in force at a reduced premium [S1] [S7] — a second transition, not one benefit with two amounts — so ln_amount() raises rather than approximating it. It cannot arise here anyway: the ¥30,000,000 cap is per insured, aggregated across that insurer’s contracts [S1] [S7] [S8] [S12], so at the composite’s ceiling it is exactly reached and reduces nothing. ln_cap_binds() tests that with a strict inequality and is False on model point 9, which sits on the boundary.

Sign convention

The notes’ CF(t) is already income positive — they write + = inflow — which is the library-wide sign of net_cf, so there is no outgo-positive liability_cf companion to publish: one stream, one sign, one name. Lapse and the renewal decline contribute no term to net_cf at all; they act only through pols_if.

Premiums fall at the start of the month they are due in and claims at the end of the month they arise in. There is no half-year correction to make and none to double-count: the annual model needed one because it collected a whole year’s premium from lives that could exit in month two and booked their claims twelve months late, and this one does neither.

Naming

Cells names follow lifelib’s basiclife/BasicTerm_S wherever it has an analogue — pols_* for counts, plural nouns for cash flows, *_rate for rates, *_pp for per-policy amounts, claims(t, kind), pols_if_at(t, timing). The full map is in the Projection docstring. Six cases needed care:

Notes	Cells	Why
q(t) and qbar(x, m)	mort_rate / mort_table_mean	Different bases, not one quantity at two arguments: the decrement is best-estimate, the premium scale’s shape parameter is the unadjusted table rate
(margin removal)	mort_be_factor	The multiplier turning the shipped valuation table into the projection’s best-estimate basis carries one name across all nine models. Not mort_ae_factor: an actual-to-expected ratio is a different quantity, measured rather than assumed
roll-forward identity	check_pols_roll_fwd() / check_pols_roll_fwd_resid(t)	The per-period in-force roll-forward check has one name in every model of this library and both sister libraries, so one test calls it across all of them
P_m(k), P_a(k)	premium_mth_pp(t), prem_pp(t)	Indexed by the term in the notes and by the policy month here, so every cash flow line is indexed alike; term_index(t) resolves it and check_prem_level() asserts the premium is still level within each 保険期間. prem_due_pp(t) is the third of the family and the only one that is a cash flow: what the payment mode makes fall in month t
d(t)	decline_rate	Deliberately not any variant of lapse. Different month, different population, different size, and not even the same kind of quantity — a one-off proportion rather than a rate per unit time — so it is the one decrement here with no *_mth companion
lap(t)	pols_lapse_pool	A stock — the lapsed lives still inside the three-year window — where pols_lapse is the month’s flow into it

There is deliberately no cv_pp and no account value. The absence is a product fact, and it is why this chassis carries a plain lapse model while the whole life technical notes (終身保険) carries 自動振替貸付.

Standardizations used

mort_be_factor = 0.80 (the largest single lever and the least evidenced number); the lapse curve 9 / 7 / 6 / 5.5 / 5 percent; decline_base = 0.15 with decline_beta = 0 and the elasticity cap decline_max = 0.50; sel_lapse_ref = 1.0, the cohort at issue; expense_acq = 15,000, expense_maint = 4,000 inflating at inflation_rate = 0.01, expense_claim = 30,000; comm_init_rate = 0.50 and comm_renewal_rate = 0.05; the premium extension above the published age-50 cell and the annualization P_a = 12 P_m; ln_interest_rate = 0.02 and ln_take_up = 0.10; wop_inc_rate = 0.0008 with wop_rec_rate = 0; reinstate_rate = 0.10 over a reinstate_window of 3 years, the window itself being sourced [S1]; the log-linear interpolation of the mortality table; and death-before-lapse-before-decline as the processing order. Each rationale is in technical-notes.md and in the cells docstring that uses it.

Four of them are arbitrary placeholders, and are labelled as such rather than dressed up as estimates: ln_take_up = 0.10, ln_interest_rate = 0.02, wop_inc_rate = 0.0008 and reinstate_rate = 0.10. No retrieved document gives an acceleration take-up, a rider discount rate, a 別表4 accident-disability incidence or a reinstatement rate; no observed range can be quoted for any of the four; and nothing in the sources bounds them. Three are deliberately round so that no reader mistakes them for measurements, and the fourth (wop_inc_rate) is explicitly not scaled off mort_rate(), because 別表4 is a much lower bar than the 別表3 test the table’s 高度障害 loading covers and a derivation off q would be false precision.

The only defence any of the four has is the switch: the module each one drives is off in the base run, so the worked example and every figure this model publishes are independent of all four. Each is live on exactly one model point — 6 and 9 for ln_take_up and ln_interest_rate, 7 for wop_inc_rate, 8 for reinstate_rate — and what those points demonstrate is the mechanics of the module, never the level. Two of the four have a bounded and stateable effect even so: ln_interest_rate enters halved, so it moves the accelerated payment by 0.5% per percentage point, and reinstate_window = 3 beside reinstate_rate is sourced [S1] where the rate is not. Replace all four before reading anything off model points 6 to 9.

Tests

tests/test_term_life_jp.py asserts the notes’ worked example hard-coded, so a reviewer can check it by eye: the t = 0, 1, 2, 11, 12, 119 and 120 rows to the yen, l(t) to six decimals, the renewal ladder ¥974 → ¥1,823 → ¥3,933 → ¥8,976 → ¥23,881 with l(120) = 0.466683 through l(600) = 0.026042, the t = 119 exit split 0.00003972 / 0.00235186 / 0.08235591, and 600-month undiscounted totals of ¥457,507.04 of premium, ¥302,433.43 of claims and +¥47,254.64 of net cash flow against −¥16,071.24 over the first 120 months — plus the decline sensitivity at all three of the notes’ points (+¥86,882.78 / +¥47,254.64 / +¥20,786.34). It also pins the frame itself: the result_cf() index is range(proj_len()), so it starts at 0 and ends at proj_len() - 1.

Two tests are about the conversion rather than about the product. One asserts that pols_if(12 j) reproduces the annual-step model’s survivorship at every anniversary, which is what makes the two runs comparable; the other that twelve monthly decrements compound back to the annual rate exactly, (1 - q_m)^12 = q and (1 - w_m)^12 = w, so a reader can see that the grid changed and the basis did not.

Each of the notes’ twelve pitfalls earns a test named after it — that 高度障害 is not a second decrement, that 更新 reprices without re-issuing, that truncation shortens the term and not the horizon, that a failed first renewed premium is an expiry rather than a mid-term lapse, that the living-needs cap is exactly reached and does not bind, and the rest. The eight optional modules are asserted in both positions.

Five check_* cells assert the identities continuously, each with a per-t signed residual at check_*_resid(t): check_pols_roll_fwd (the roll-forward, with the 復活 inflow as its own term), check_lapse_pool (the pool’s one inflow and two outflows), check_pols_payer (payers and waived lives partition the in-force), check_prem_level and check_net_cf (the statement’s columns add up to its own total). All five return True on all nine model points. Four of them close to roll_fwd_tol = 1e-12, an identity between cells evaluated in one expression; check_net_cf closes to a separate named cash_tol = 1e-8, because it re-reads yen amounts of order 1e5 back out of the result_cf() DataFrame and the round trip leaves float64 rounding the tighter tolerance would reject. cash_tol is still far below one yen, the smallest error a reader adding up the printed statement could see. tests/test_model_conventions_jp.py adds the house style, parametrized over the registry rather than restated here.

python -m pytest tests -q